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Former ARPA-H director Renee Wegrzyn wants to solve science’s ‘magic hands’ problem
With her new startup, Transfyr, Wegrzyn is using AI to help medical breakthroughs make it out of the lab. She explains why that matters, and why she fears America is losing its scientific edge.
Did an Affordable Audiophile Brand Just Revive the Classic iPod?
Best of all, it's substantially more affordable than you'd probably guess.
Costco Is Quietly Offering an Underrated, Discontinued Omega Watch Online
Costco is currently offering a discontinued annual calendar dress watch from Omega as an online-only special.
The captain from Bolton who helped with Titanic rescue
Arthur Rostron was the captain of RMS Carpathia when it received a morse code message from the ship.
Seiko’s New Affordable Chronograph Takes the “Panda” Moniker to Its Logical Conclusion
You've never seen a "panda" dial watch quite like this before.
G-Shock's Most Expensive Watch Ever Gets a Way More Affordable Everyday Edition
While sharing the same inspiration and iconic square case, this new edition costs over 99 percent less, making it far more accessible.
13 Cool New Gadgets to Keep on Your Radar
Catching you up on the past week’s most notable product releases.
Christopher Ward Brings Back a Modern Classic, with Serious Improvements
Christopher Ward reimagines its original jump-hour watch with a new limited edition featuring an elevated design and a more compact size.
G-Shock's New Stunner Is Eight Times the Price of a Rolex Submariner
Assembled without case screws, the MRGD5000 is G-Shock's most premium watch to date. It's not particularly close.
Anthropic joins ARPA-H clinical AI moonshot, will hold closed-door health care event
In this edition of STAT Health Tech: Oura delays IPO plans, Anthropic joins an clinical AI effort by ARPA-H, and MAHA's health data goals.
13 Cool New Gadgets to Keep on Your Radar
Catching you up on the past week’s most notable product releases.
DARPA wants to give the military something much better than GPS
GPS can be jammed. DARPA is making clocks that could keep military systems running without it and eventually enable completely new capabilities.
This Tiny Audiophile Gadget Brings CD-Quality Hi-Fi to Your iPhone
iPhones don't stream the best audio quality over Bluetooth. The good news? You can change for less than $100.
Switzerland's Hottest Watch Brand Keeps It Rolling with a Revamped Water-Friendly Sports Watch
Breguet keeps its makeover train rolling, this time outfitting the Marine with a cleaner dial design across three-hand and chrono models.
Is metascience being co-opted?
In July, the White House released a report that argued academia is deeply dysfunctional and in need of radical reform.Science: A New Golden Age sets out a litany of problems many academics will recognise: interminable waits for grants; short-term funding rather than support for longer, more ambitious projects; ballooning administrative requirements; pressure to publish, and many more. “The scientific machine is getting bogged down,” it says. At least on the face of it, Science: A New Golden Age is a triumph for the metascience movement, a loose-knit, global group of academics, philanthropists and think tankers that want far more testing and experimentation in how we fund public research and innovation. The report tells US research funders to set up internal metascience units to do just this. But it has also set alarm bells ringing that metascience is being conveniently deployed to help justify the defunding of US universities, which US vice president JD Vance has openly labelled “the enemy.” “Just because you have an issue with your plumbing in your house doesn't mean you light the entire house on fire and burn it to the ground,” said Colette Delawalla, chief executive of Stand Up for Science, a group campaigning against what it says are attacks on scientists by the Trump administration. No one is arguing that academia doesn’t need reform, but there’s debate over how far and how fast the system should be shaken up. Does public R&D need slow and steady evidence-based adjustment, or radical new experiments?One of the key arguments in Science: A New Golden Age is that there’s not enough funding for what it calls “mid-level” science: engineering-heavy projects that require tens of millions of dollars, involve ten to a hundred people, and last around five years. Such a project might develop a brain-computer interface for people with Alzheimer's disease, for example. As such, the US National Science Foundation (NSF) has already announced $1.5 billion to set up so-called X-Labs, effectively new research organisations to pursue breakthroughs in areas such as quantum sensing and new scientific instruments. Funding divertedBut there’s a catch: this money has squeezed NSF budgets for more traditional, fundamental research grants, Science reported in June, likely meaning less cash for university-based scientists. Science: A New Golden Age also makes various swipes at universities, accusing them of extracting overheads from scientists to fund administrative bloat. “We have become dependent on a narrow set of legacy institutions,” it says. The report doesn’t mention it, but the US government is simultaneously seeking greater powers over federal funding, which would allow political appointees at funding agencies to screen or cancel research grants mid-project. The White House is also seeking to slash the NSF’s budget by more than half in 2027, although similar cuts proposed for 2026 were blocked by Congress. Convenient hookThis has worried some that the metascience agenda could be misused as a kind of smokescreen to further weaken universities. James Wilsdon, executive director of the Research on Research Institute at University College London, said that the report contains lots of “interesting ideas” and agrees that in places, academia is a “pretty broken system.” But the risk is that metascience is being deployed as a “convenient hook” to support cuts and a “political land grab” to transfer grant power from scientists to political appointees in US funding agencies.So far, the metascience agenda hasn’t been seized on by radical right parties in Europe. But Wilsdon worries it will cross the Atlantic, politicising research reform efforts and making them more difficult to carry out. The report is already making some in Europe sceptical of the entire idea. “The system is not without flaws, but the metascience discussion is not likely to improve the system but rather to generate munition to undermine it,” said Claes de Vreese, a professor of AI and society at the University of Amsterdam. Peter Thiel protégéOfficially, Science: A New Golden Age has just one named author: Michael Kratsios, the director of the Office of Science and Technology Policy and a former chief of staff to Silicon Valley mogul Peter Thiel. He also served as chief financial officer of Thiel’s Clarium Capital Management. Since the 1990s, Thiel has criticised US universities for moving away from teaching what he views as the classic works of western civilisation, and drifting towards “anti-Western zealotry.” And since 2011, he has offered two-year fellowships of $250,000 to young people to skip or drop out of university, and instead start companies or pursue scientific research. “College can be good for learning about what’s been done before, but it can also discourage you from doing something new,” according to the Thiel Fellowships website. In other areas, the Trump administration has promoted academics with a longstanding interest in metascience. For example, the head of the National Institutes of Health, Jay Bhattacharya, in 2020 co-authored a study arguing the funder backs overly conservative proposals, although he achieved wider prominence as a critic of pandemic lockdowns. Careless talk costs fundingEven before the Trump administration embraced the metascience agenda, some metascientists privately wondered whether they should be careful not to overstate dysfunctions in academia, in case this gets used as ammunition against universities. But others think being cautious with criticism is pointless. After all, last year, the Trump administration sought to cut funding from US universities on an entirely different premise: supposed antisemitism on campus. “I don't think these governments need a technical metascientific excuse to attack the universities, they will want to do so anyway,” said Will Stone, a researcher at Science Works, a new think tank advocating for a “metascience revolution” in the UK. “I think researchers should report what they find, even when the findings may be used to advance policies they disagree with,” echoed Russell Funk, a researcher at the University of Minnesota, who in 2023 co-authored a highly influential paper that argued scientific papers were becoming less disruptive over time. No justificationThat said, Funk thinks that metascientific research certainly doesn’t justify a weakening of universities. Instead, scientists need “time, resources and stability to pursue unfamiliar ideas,” he said. “Taking away those conditions works against the very goal the reforms are supposed to achieve.”The Institute for Progress, a Washington DC-based innovation policy think tank that has championed X-Labs and metascience units, also says that reforms should complement, not supplant, universities. “Unpredictable, unreliable, and reduced federal funding to researchers is destabilising to the very foundations of research and development and is bad,” said Jenn Gustetic, the institute’s director of metascience and R&D policy.Evolution or revolution?This points to a much bigger divide in the metascience community: how far and how fast should public R&D be shaken up? Does it need evolution or revolution? For example, Science: A New Golden Age says the science system relies too heavily on traditional “workhorse” project grants, the type that pay out around $250,000 a year to fund “hypothesis-driven science by small teams on tractable questions.” However, in criticising project grants, the report “decisively picks one side of a debate that is far from settled,” David Hilmer Rex, a metascientist based in Denmark, has written. In June, Pierre Azoulay, a metascientist at the Massachusetts Institute of Technology, defended project grants, saying that the alternative of X-Labs “merits scepticism.”A-B testingOne challenge in metascience is that it’s easier to generate evidence for some types of change than others. Most straightforwardly, research funders can run A-B tests on different ways of awarding money. For example, Germany’s Volkswagen Foundation recently trialled distributed peer review, where applicants rate each other’s proposals, and measured its results against traditional review panels. Distributed peer review made quicker decisions, although awarded money to largely different ideas. However, much of the focus of the metascience movement has been on founding fast-moving and free-acting new research organisations and funders, in contrast to the alleged stodginess of mainstream academia or public innovation grants. A culture of speed and freedom are the animating principles behind Germany’s Sprind or the UK’s Advanced Research and Invention Agency (Aria).Related articlesViewpoint: it's time to experiment with how Europe funds science European researchers sound the alarm that Trump rule changes will hit cooperationHowever, while it’s possible to closely study previously successful bodies, such as the US Defense Advanced Research Projects Agency (Darpa), for historical clues to their secret sauce, it’s impossible to guarantee in advance that the likes of Sprind or Aria will actually work as hoped. But advocates of new organisations say this inherent uncertainly shouldn’t stop us trying. “If we wait for definitive proof that something will work before we try it, I don't know where your proof is coming from,” said Stone. Trade-offsUp until now, experiments in metascience haven’t generally come with hard trade-offs over money. In the US, new ideas such as focused research organisations have often been funded by billionaire philanthropists. In Europe, new innovation agencies such as Sprind and Aria, or the Netherland’s new National Agency for Disruptive Innovation, haven’t needed explicit cuts elsewhere in the research system. But with the $1.5 billion establishment of X-Labs by the NSF, the trade-offs are becoming clearer. Some metascientists privately wonder whether a smaller pilot would have been more appropriate, given that the idea is untested. The Institute for Progress counters that the $1.5 billion X-Labs budget is spread out over a decade, so will account for less than 2% of the NSF’s annual budget. “It’s important to remember that [Darpa] was once an institutional experiment as well. Now it’s one of the most cited innovative funding models in the world,” said Gustetic.As for Europe?The US isn’t the only country placing metascience at the heart of its research strategy. Science: A New Golden Age looks admiringly at the UK, which in 2024 set up a Metascience Unit to scrutinise how it funds research. In Brussels, it’s more of a mixed picture. In 2018, the EU set up the European Innovation Council to funnel grants and equity to start-ups, although it’s sometimes criticised for awarding money too slowly. Recently, the council has started experimenting with challenges, where teams of inventors compete to overcome a technological challenge. The European Commission also wants to experiment with partially randomising grant winners, to cut down on bureaucracy. However, the Commission’s own advisers have said the EU’s own research schemes are too conventional. They want the Commission to establish a new dedicated unit to scrutinise and experiment with funding. But there’s no sign of a such unit emerging in Brussels’ plans for the next Horizon Europe programme, which from 2028 is set to distribute more than €160 billion over seven years. Sam Bogerd, director of breakthrough innovation at the Brussels-based Arq Foundation, an AI-focused think tank that also looks at metascience, says that Europe might have the opposite problem to the US: universities and basic research funding are still reasonably well supported, but there’s not enough experimentation. “We're not taking a close enough look at where this money can achieve the most, and I am worried that the good ideas around metascience will be tainted politically by their association with the Trump administration,” he said. He’s still lobbying for the Commission to set up an experimental unit. But in the meantime, the Arq Foundation plans to do its own research, and is currently hiring two metascience researchers to run the rule over how Brussels spends research funding.
This $630 Scratch-Resistant Adventure Watch Leans on Classic Omega Vibes
Héron debuts the Continental, an automatic go-anywhere, do-anything design inspired by Jet Age adventure watches.
U.S. Army Pacific Builds Mental Health Resilience and Community Empowerment Across the Federated States of Micronesia
FEDERATED STATES OF MICRONESIA— The U.S. Army Pacific (USARPAC) Oceania Engagement Team (OET) and USARPAC Chaplain Directorate recently completed a two-...
Vanderbilt Health to lead nationwide initiative to improve outcomes for critically ill patients
Funded via ARPA-H, CIRCLE will combine advances in diagnostics and AI to speed development of next-generation interventions that identify and respond to harmful immunoinflammatory states before life-threatening complications occur.
This 1970s-Inspired Automotive Sports Watch Cleverly Reinvents the GMT
Singer Reimagined's latest GMT has 1970s-inspired looks and an advanced in-house dual-time movement with unique functionality.
Zelenskyy welcomes leaders from 7 countries for a regional summit
KYIV, Ukraine (AP) — Ukrainian President Volodymyr Zelenskyy hosted leaders and representatives from seven regional countries for talks on deepening cooperation on Friday, as overnight Russian strikes in multiple regions sparked fires and caused several deaths and injuries. The inaugural summit of the so-called “Carpathian Eight” brings together Ukraine, Romania, Serbia, Poland, Slovakia, the Czech […]
Christopher Ward Takes on the Big Brands with Its First Automatic Chronograph in Six Years
Christopher Ward introduces an automatic chronograph to the C63 Sealander collection, debuting in five dial colors.
Rolex's Surprise New Watch Boasts Arguably Its Most Advanced Movement Yet
Rolex reintroduces the Padellone to its lineup, this time as a Perpetual dress watch with an annual calendar and Dynapulse escapement.
Seiko's Affordable "Fifty Five Fathoms" Dive Watch Gets a Striking Makeover
Seiko updates one of its best-looking affordable diver-style watches with classy new green and white colorways.
ARPA-H to invest $62 million to develop FDA-authorized AI to help treat heart failure
ARPA-H, the government agency that funds cutting edge health research, plans to commit $62.7 million to develop artificial intelligence bots that direct
Scientists covered 200 square metres of a Swiss glacier with sheep’s wool; early tests found it protected the ice about as well as plastic sheets, but only 0.02% of Swiss glacier ice is covered
A natural wool covering placed over 200 square metres of high-altitude Alpine ice has proved as effective at slowing summer melting as conventional synthetic tarpaulins, offering a possible biodegradable alternative to plastic sheets. Initial results from the "Keep It Wool" trial on the Tsanfleuron Glacier in western Switzerland show that prototype wool blankets protected snow and ice from heat about as well as plastic geotextiles.
Citizen’s Swiss Sister Brand Drops a Mechanical Movement into Its Best-Looking Watch
Frederique Constant adds to its surging Moneta line of dress watches by equipping it with a mechanical movement for the first time.
My Favorite Sleeper Sports Watch Gets a Performance-Minded Makeover
H. Moser & Cie. continues its partnership with Alpine Motorsports by launching a new Streamliner combining a ceramic case and a tourbillon.
How a former ARPA-H director's startup is tackling AI's ‘dumb problems’
In this edition of STAT's AI Prognosis: A startup tackling the 'dumb problems' at the intersection of AI and bio, accuracy of AI scribes, and more.
UC San Diego Receives Up To $11.3 Million to Develop Smarter Red Blood Cells
Researchers at UC San Diego have been awarded a DARPA contract to redesign red blood cells to temporarily boost human performance.
Longines Improves Its Classic, Complicated Chronograph. It's Still an Absurd Bargain
Longines continues its Master Collection overhaul with an update to the line's most iconic watch, the Moonphase Chronograph.
Longines Drops an Elegant Monopusher Chronograph for Way Less Than You'd Expect
Longines adds a monopusher chronograph to its Master Collection for the first time, powered by a new, Longines-exclusive movement.
21 New Watches You Missed from Timex, Blancpain, Grand Seiko and More
Keep time with the industry's most exciting new watches, including Grand Seiko, Blancpain, Timex, Casio and more.
8 Cool New Gadgets to Keep on Your Radar
Catching you up on the past week’s most notable product releases.
20 New Watches You Missed from Seiko, Timex, IWC and More
Keep time with the industry's most exciting new watches, including Seiko, Casio, Timex, IWC TAG Heuer and more.
11 Cool New Gadgets to Keep on Your Radar
Catching you up on the past week’s most notable product releases.
Bexar County health department at risk as ARPA money runs out
Just four years in, Bexar County's public health department faces an uncertain future as the pandemic-era funding it relied on dries up.
13 Cool New Gadgets to Keep on Your Radar
Catching you up on the past week’s most notable product releases.
The Top 10 New Cars We’re Most Excited for This Year (So Far)
Feeling left out of the loop in terms of new car reveals, announcements and launches this year? Don't worry, we got you covered.
Seiko's $750 Speedtimer Chronograph Gets a New "Edo Purple" Dial
The affordable Prospex Speedtimer, equipped with a solar quartz movement, receives a "purple panda" dial revamp.
Viewpoint: Europe needs a new scientific contract
The White House's new science strategy should be required reading in the Berlaymont and every European capital.Not because Europe should copy it. Parts of it should concern Europeans deeply. But because it asks the question Europe has so far avoided while steadily losing ground in the global race for technological leadership.Europe already possesses many of the world's finest universities, scientists and research-intensive companies. It produces world-leading research in fields ranging from quantum technologies and biotechnology to climate science. Yet time and again Europe generates the scientific breakthroughs while others build the companies, industries and strategic advantages around them, particularly in digital technologies. Increasingly, we find ourselves regulating technologies whose underlying capabilities were developed elsewhere.This is not primarily a scientific failure. It is an institutional one.The settlement that shaped modern science policy emerged after the Second World War. In Science, the Endless Frontier, Vannevar Bush argued that governments should invest in fundamental research while protecting scientists from political direction. Public investment was justified precisely because no government, university or company could predict where the next transformative discovery would emerge.That scientific contract served liberal democracies extraordinarily well. It underpinned decades of advances in medicine, computing, energy and communications, but the world for which it was designed no longer exists.Artificial intelligence is transforming not only technology but the production of knowledge itself. Advanced chips, computing power, data and scientific instruments have become strategic assets. Scientific knowledge now diffuses globally almost instantly. Competitive advantage increasingly lies not in making discoveries first, but in possessing the infrastructure, capital, engineering capability and institutions required to translate those ideas into products, industries and strategic advantage.The old boundaries between science, industrial policy, national security and geopolitics have largely disappeared. Dual-use technologies now underpin both economic power and military deterrence.The US has recognised this shift. In July, the White House published Science: A New Golden Age, a comprehensive attempt to rethink the relationship between science and national power. The report builds on Bush's legacy, but departs from his framework in important ways. Science is no longer treated simply as the source of future discoveries. It is presented as the foundation of economic resilience, technological leadership and national security. AI, advanced infrastructure and the capacity to build become as important as discovery itself.Yet the report also reveals a striking paradox. The same administration calling for “a new golden age” of US science has simultaneously proposed deep cuts to the federal research enterprise while expanding political influence over the allocation of public research funding. Europe should therefore distinguish between the report's diagnosis and its prescription. The diagnosis, that science has become a strategic asset, is compelling. But the prescription is self-defeating. Long-term technological leadership depends on strong universities, academic freedom and the institutional autonomy that makes scientific discovery possible. This distinction matters. Bush's achievement was never simply to make science serve national strength. It was to establish a division of labour in which governments invested in research without attempting to direct discovery itself. Scientific breakthroughs emerge precisely because researchers retain the freedom to pursue questions whose value cannot yet be demonstrated and whose conclusions cannot be anticipated.Neither China, nor the USAs science becomes more strategically important, the temptation to politicise it inevitably grows. But scientific freedom is not an obstacle to technological leadership, it is an essential precondition.China represents the opposite model. For decades it has integrated science, manufacturing, infrastructure and industrial policy into a single national strategy. Its ability to mobilise resources around critical technologies is formidable. But it does so within an authoritarian system where scientific priorities are increasingly inseparable from political power.Europe should imitate neither model. It should learn from the strategic ambition of the US without importing its growing politicisation of science. And it should learn from China's capacity to build without adopting its concentration of power.Instead, Europe needs a scientific contract of its own. Bush's scientific contract protected the freedom to discover because the defining challenge of the 20th century was producing new knowledge. Europe's next scientific contract must preserve that freedom while ensuring that scientific discoveries can be converted into technological, industrial and strategic capability. Because in a transactional world, Europe's freedom to shape its own future depends on its capacity to build it.Related articlesThe US ‘golden age’ science strategy: what Europeans need to knowEuropean researchers sound the alarm that Trump rule changes will hit cooperationNIH turmoil risks reciprocal EU-US access to health research fundingThis does not mean subordinating free science to competitiveness or industrial policy. On the contrary. Scientific autonomy remains the first principle of Europe's next scientific contract. Governments should not direct scientific discovery. Their responsibility is to ensure that Europe's scientific, industrial, financial and regulatory institutions work together rather than in isolation.Meeting that responsibility requires far more than research funding. European researchers need access to world-class computing infrastructure, advanced laboratories, shared datasets and scientific facilities. Innovative companies need patient capital, testing environments and a genuine single market in which they can scale without facing 27 different regulatory regimes. Public procurement must evolve from purchasing existing technologies to creating markets for new ones. Regulators must become partners in experimentation rather than guardians of yesterday's rules.A connecting architectureEurope does not need to invent the Defense Advanced Research Projects Agency (Darpa) model from scratch. Britain's Aria, Germany's Sprind and Wellcome Leap already show that Europe can build institutions capable of taking high-risk, mission-oriented bets while preserving democratic accountability. The challenge now is not to create more isolated initiatives, but to connect them to Europe's broader scientific, industrial and financial system so that breakthrough discoveries can scale.Above all, Europe must stop treating research, innovation, industrial policy, finance and security as separate policy domains. The challenge is no longer to create excellent institutions in each of these fields, but to make them work as one European innovation system. Europe's strength will increasingly depend not on excellence within institutions, but on excellence across them.Europe's ambition should be neither to imitate the US nor to emulate China. It should demonstrate that scientific freedom, democratic legitimacy and technological capability are not competing objectives, but mutually reinforcing strengths. In an age of geopolitical rivalry, Europe's greatest advantage may ultimately be its ability to attract the world's best talent by combining scientific freedom, democratic openness and technological ambition.Bush's scientific contract helped define the 20th century because it protected the freedom to discover. Europe's challenge is not to abandon that achievement but to extend it. The defining task of Europe's next scientific contract is to preserve the freedom to discover while building Europe's capacity to translate scientific discovery into technological power.Kaare Nielsen is founder of Pomus, a Danish think tank on science and innovation.
17 Awesome Motorcycles You Might’ve Missed from Honda, Harley-Davidson, Kawasaki and More
The red brand has been red hot of late, but other manufacturers (and auction houses) are making noise too.
10 Cool New Gadgets to Keep on Your Radar
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TAG Heuer Adds Some 1980s Japanese Swagger to the Carrera Chronograph
In collaboration with Bamford Watch Dept and Team Ikuzawa racing, the 'Glassbox' Carrera gets a gorgeous red-and-white retro revamp.
Aston Martin’s New Stunning GT Will Even Make 007 Come out of Hibernation
Aston Martin reveals its new exclusive set of limited-production Vanquish models, all to commemorate the model's 25th anniversary.
When It Comes to Fusion, Materials Matter
Fusion at high temperatures powers the sun and, if harnessed, could provide a potential source of energy here on Earth. But controlling fusion reactions has other benefits. The process also generates subatomic particles called neutrons that are used in a range of applications spanning medicine, research, and national security. Now, scientists at the University of California, Davis (UC Davis), and the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) have found that the materials surrounding a fusion reaction can dramatically increase how often it occurs, particularly at low energies where fusion is rare. The study was published July 18 in Nature Communications. Their approach sets up a way to study and engineer nuclear reactions within solid materials, opening a new field of “materials-driven fusion.” Instead of designing materials just to survive the harsh conditions of fusion, researchers might be able to design materials that boost the reaction in specific conditions, similar to the way catalysts speed up chemical processes. “It gives you a new knob to turn that you didn’t have before,” said Arun Persaud, head of the Fusion Science & Ion Beam Technology group in Berkeley Lab’s Accelerator Technology & Applied Physics (ATAP) Division. “If we understand this effect better, it opens the door to engineering new materials that would affect the fusion rate under certain conditions. Someday future progress might enable more compact and efficient neutron generators, which have all kinds of applications, like cargo screening, planetary science, and medical therapy and imaging.” In the experiment, researchers used two different methods to pack deuterium, a heavy form of hydrogen atoms that is often used in fusion, into thin foils of palladium and titanium. They then fired a beam of deuterium ions at the foils at different energies and measured how often fusion happened. Then they compared the rates from the different materials and methods with the “bare” fusion reaction (not in a material). The team found that fusion rates depended on how the deuterium was loaded into the metal foils. The biggest effect was at the lowest energies, below 2.5 kiloelectronvolts (keV), where theory predicts fusion rates drop off sharply. Instead, researchers found a surprising plateau: Some samples showed elevated fusion rates roughly a quintillion times higher than bare fusion reactions. (A quintillion is a 1 followed by 18 zeroes.) Researchers are not exactly sure why that’s happening, though they have some ideas and ways to test them. The electrons and defects within the material might partially shield repulsive electrostatic forces between deuterium nuclei, making it easier for them to get close together and fuse. Tuning the electronic structure, defects, and composition of materials could help make nuclear reactions happen more readily. “It comes down to better understanding the mechanism so that we can try to enhance it,” said corresponding author Jeremy Munday, a professor at UC Davis. “We’ve seen that we can increase fusion rates, but what is the limit? Can we bring it to lower temperatures or energies? There’s obviously a lot of interest and excitement about fusion, so if there’s something we can learn about the physics at these lower energy scales, maybe there’s something we can translate to other areas of nuclear science.” The team plans to explore a wider range of materials and continue probing the unexpected fusion plateau at lower energies. Their work establishes a reproducible experimental platform to study how solid materials influence nuclear reactions, creating a new area of research that links fusion science with materials science and chemistry. “The work shows conclusively that the material environment where fusion occurs at low temperatures is an active participant rather than a passive container,” said Cameron Geddes, director of ATAP. “That adds a new dimension to fusion research.” This work was funded by the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E). ### Lawrence Berkeley National Laboratory (Berkeley Lab) is committed to groundbreaking research focused on discovery science and solutions for abundant and reliable energy supplies. The lab’s expertise spans materials, chemistry, physics, biology, earth and environmental science, mathematics, and computing. Researchers from around the world rely on the lab’s world-class scientific facilities for their own pioneering research. Founded in 1931 on the belief that the biggest problems are best addressed by teams, Berkeley Lab and its scientists have been recognized with 17 Nobel Prizes. Berkeley Lab is a multiprogram national laboratory managed by the University of California for the U.S. Department of Energy’s Office of Science. DOE’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States, and is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science.
Garmin's New Whoop Challenger Is Also a Shot at Tech's Favorite Business Model
The Circa Smartband is a deliberate counter to the subscription-gated model that defines Whoop and Fitbit's latest tracker.
REI Is Quietly Selling Arc’teryx’s Super Light Trail Jacket Way Below Retail
You can score this coveted garment for up to 40 percent off, assuming you aren't too picky about the color.
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Fitbit's Whoop Challenger Quietly Added a Game-Changing Upgrade
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Honda’s Affordable New Performance Moto Is Its Most Rider-Friendly Yet
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Sunday Preview – Ogor Mawtribes on the warpath
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Honda’s New Small, Affordable Retro Moto Is Super Rider-Friendly Too
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A Japanese Hi-Fi Specialist Just Gave Audiophiles a New Reason to Get Excited
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Karpa Health Launches Turnkey Nationwide Telehealth Platform as GLP-1, Peptide, and Hormone Demand Accelerates
The all-in-one platform helps entrepreneurs, clinics, med spas, gyms, and wellness brands launch cash-pay telehealth programs nationwide. TAMPA, FL / ACCESS Newswire / July 3, 2026 / Karpa Health today announced the general availability of its turnkey ...
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Is SMIC N+3’s Metal Pitch Smaller than Intel 18A’s?
Almost four years ago, we published that SMIC had started shipping 7 nm (N+1) chips. Now, SMIC is shipping its third-generation 7 nm (N+3) process in Huawei’s Kirin 9030, with a minimum metal pitch of 32.5 nm, about 10% tighter than the 36 nm minimum metal pitch shipping in Intel’s latest Panther Lake CPUs on 18A. The headline is true, but incomplete cherry picked metric. N+3 reaches the density of TSMC N6 through aggressive DUV multi-patterning and design-technology co-optimization (DTCO), but it pays for that in complexity, efficiency and process control. We found this and more in our reverse engineering and teardown where we cover SMIC’s N3 process technology, Huawei’s packaging, memory, architecture, and more. SemiAnalysis has been building a state-of-the-art teardown lab in Oregon capable of analyzing the world’s most advanced and important chips over the last year and half. We have already generated revenue on advanced datacenter chip teardowns including our recent reverse engineering of a major TSMC customer’s COUPE CPO optical engine + EIC 3D stack. This is the first public report from the SemiAnalysis Teardown Engineering & Evaluation Lab, or STEEL for short. The lab is aggressively scaling up and out and we’re excited to announce it publicly. This is a bit of inconvenient timing for TechInsights as they are private equity owned and currently being sold while having enjoyed virtually no credible competition for decades. This has led to TechInsights underinvesting in CAPEX. SemiAnalysis exceeds TechInsights in revenue despite no venture or private equity ownership and being founded only 6 years ago. Because we have no external investors and are founder led, we move faster, build faster, and we can release client chip teardowns for free regularly, while focusing on datacenter for our major clients. Here’s the first public image from our lab, the HiSilicon Kirin 9030 Pro SoC: This report will detail our teardown of the Kirin 9030 and our findings on SMIC’s N+3 process, the most advanced in China. For comparison, we’ll show our teardown of the MediaTek Helio G99, made on TSMC N6. Through this comparison, we can look at the effect of export controls – SMIC N+3 and TSMC N6 are comparable nodes, but one is heavily export-controlled, the other free to use the West’s most advanced equipment. Here we see both China’s progress and constraints. SMIC N+3 reaches TSMC N6-class logic density, but it requires far more aggressive DUV multi-patterning, so it does not match N6 on process maturity or cost. The Kirin 9030 Pro performs similarly to three-year-old Android flagships, and trails far behind the current flagship SoCs from Apple, Qualcomm, MediaTek, and Samsung. The efficiency gap is even wider. Export controls have not stopped Huawei and SMIC from shipping advanced silicon, but they have forced a different path. Without EUV, SMIC is leaning harder on DUV multi-patterning, DTCO, and increasingly complex integration. The roadmap continues forward through tighter design rules and backside power, but each step adds cost and process risk. Huawei’s τ scaling and LogicFolding show another path: stacking active logic and recovering density through advanced packaging and system-technology co-optimization (STCO). To understand the Kirin 9030, we must first understand Huawei’s SoC history. HiSilicon is Huawei’s chip design arm, responsible for the Kirin smartphone SoCs, Kunpeng server CPUs, Ascend AI accelerators, and switch/router networking silicon. Before export controls, Huawei was TSMC’s largest customer – the only customer on TSMC’s first EUV node, N7+, and among the first on N5, alongside Apple. That ended in late 2020. Huawei switched to Qualcomm SoCs in its flagship smartphones, though export controls limited them to 4G-only variants. In late 2023, Huawei returned to in-house silicon with the Kirin 9000s, a successor to the Kirin 9000, fabricated on SMIC N+2 instead of TSMC N5. In the following years, they released the Kirin 9010 and 9020 on the same N+2 process. These chips used Huawei’s in-house TaiShan CPU cores and Maleoon GPU. We have not torn down a Kirin 9020 ourselves, so the predecessor die shot is from Kurnal. The die shots show how Huawei spent its silicon budget: which functional blocks are where, and how their areas compare to the predecessor. First, a quick guide to the major blocks on the die. The total die area is nearly identical, but the 9030 uses that area more aggressively. A denser process lets Huawei fit an extra middle CPU core, more GPU and NPU cores, and larger caches into the same footprint. In contrast, the Helio G99 is a much smaller, low-cost SoC, built for budget smartphones rather than a flagship device. While the Kirin 9030 is ~140 mm², the G99 is only ~29 mm², roughly one-fifth the area. The underlying TSMC process technology, however, is directly comparable as a baseline for analyzing SMIC’s. The Kirin 9030 is an evolutionary refresh, not a clean-sheet design. Its CPU, GPU and NPU cores carry over the 9020’s families, and the gains come from three levers: the SMIC N+2-to-N+3 process step, DTCO and floorplan work, and incremental microarchitecture. Area is where the first two show up, and the 9030 scales well here. Performance and efficiency are the harder test. Huawei’s design holds up better than its node would suggest, but the chip still trails, both because N+3 sits behind the leading-edge nodes and because its cores, while competent, remain a few generations behind the newest designs. The new prime core is an incremental update. The main changes are a 10% frequency increase from 2.5 GHz to 2.75 GHz and a doubling in the L2 cache from 1 MiB to 2 MiB. Despite the increased cache, the core size decreased by 7.6%. Excluding the private L2 cache, the core size decreased by 21%. This is a large reduction for an incremental node. Compared with the TaiShan New V120 core in the Kirin 9020, the Kirin 9030’s middle core is almost unchanged architecturally, yet each core shrinks by ~22%. Most of that comes from the move from N+2 to N+3, with layout likely accounting for the rest. Visually, the most noticeable change is the increase from 3 middle cores to 4. There is also a 20% increase in the shared L3 cache of the big cluster. This helps to improve multi-core performance without sacrificing much in terms of area. Even with each core shrinking, the big CPU cluster’s total area is essentially unchanged. The per-core savings went back into an additional middle core and larger caches. The tiny cores shrank less than the prime core (excluding its L2 cache) and less than the middle cores. This is likely because fixed overhead is a larger share on a small core. We cannot resolve any architectural changes from the die shot alone, but the per-clock and efficiency gains shown below point to more than pure process and layout scaling. The area reduction was offset by a doubling of the shared L2 cache from 2 MiB to 4 MiB, leaving the total tiny CPU cluster area slightly larger. Area is the easiest improvement to see from a die shot, but it is only one part of PPA (power, performance, area). For modern logic, power and performance matter just as much, and often more. Since Dennard scaling broke down in the mid-2000s, voltage and frequency have not scaled in step with transistor dimensions, so each node has had to fight harder for gains in performance and efficiency. The starkest comparison is not Kirin 9020 versus Kirin 9030 Pro. Apple’s efficiency cores run circles around Huawei’s prime core. Apple’s low-power core delivers 20% higher integer performance while drawing only 1 W, compared with 4.5 W for Huawei’s prime core. N+3 matches TSMC N6, but N6 is several generations old. Apple and Qualcomm build on N4 and N3P, which are denser and sit on a better voltage-frequency curve, giving them a larger transistor budget and more performance per watt. The 9030’s own cores did improve. The middle and tiny cores gained 17% and 14% in per-clock integer performance over the 9020, with floating-point flat on the middle core and up 11% on the tiny. The tiny core improves cleanly, with performance rising while power falls and efficiency increases by 45% in integer and 24% in floating point. The middle core is mixed: integer performance rises but power rises faster, cutting integer efficiency by 7%, while lower power lifts floating-point efficiency 16%. Per-clock gains at the same or lower frequency are microarchitectural, so the cores are tuned, not just shrunk. Both also failed to hold their rated maximum frequencies, pointing to thermal, power, or stability limits. Per clock, the middle core sits around Arm Cortex-A720 and the tiny core near the Cortex-A520; absolute performance trails because Huawei clocks them much lower. The prime core is roughly Cortex-X2 class per clock, a 2021 design. Apple’s 2020 M1 Firestorm core is still 35% higher per clock and 57% faster in absolute integer performance at a similar 4.5 W. The current leading edge is further ahead again: the Apple M5 P-core is 60% higher per clock and 2.7× faster, the Arm C1 Ultra 45% higher and 2× faster. Matching older high-end cores per clock is a genuine design achievement. What Huawei cannot match is the voltage-frequency curve and transistor budget of leading-edge nodes, which let Apple, Qualcomm and others spend more transistors in the same area on wider cores, larger caches and deeper buffers while running at lower voltage. Huawei’s LogicFolding roadmap is one answer, stacking active logic to recover density and shorten signal paths. We return to it later. The GPU compute units (CUs) changed more visibly than the CPU cores, moving to a more rectangular layout for both the arithmetic logic unit (ALU) clusters and the CU overall. Even with ray-tracing support added, a CU shrank ~28%. However, that shrink is offset by the increase from 4 to 6 CUs and the area outside the CUs grew 33%. Overall, the GPU cluster is larger by ~10%. The GPU is where Huawei makes its biggest gains. The Maleoon 935 is not competitive with current flagships, but it is a large step up from the 920 and reaches older-flagship territory. In 3DMark it is 70% faster in Wild Life Extreme (WLE) and 79% faster in Steel Nomad Light (SNL) than the 920; with 11% higher clocks and 50% more CUs, the ~67% theoretical uplift roughly matches WLE and is beaten by SNL. It edges ahead of the Snapdragon 8+ Gen 1 in WLE and SNL, and the Dimensity 9200 and Apple A16 in WLE, but stays far behind newer parts: the Snapdragon 8 Elite Gen 5 and Dimensity 9500 are ~2.4–2.6× faster in WLE and ~3.2× faster in SNL. The Maleoon 935 is Huawei’s first GPU with hardware-accelerated ray tracing; there it lands slightly ahead of the Exynos 2200, and on par with the Apple A16, with current flagships up to 3.7× faster. The Neural Processing Unit (NPU) saw the largest structural changes of any block, moving from a Lite and a Tiny core in the Kirin 9020 to a Lite and two Tiny cores in the Kirin 9030. Both core types also show significant layout changes. This is a reversal in Huawei’s NPU design. The Kirin 9000 5G, its last flagship chip on TSMC N5, used two Lite and one Tiny core. The series of SoCs on SMIC N+2 moved to one Lite and one Tiny core, likely for area savings. With the Kirin 9030, Huawei has shifted back toward a larger multi-core NPU cluster, but with the additional area going to a Tiny core rather than a Lite core. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Before diving into the process stack, the package and memory are worth separating from the SoC itself. The Pro variant of the Kirin 9030 carries 12 GB of Samsung DRAM, with two stacks of four dies each. The dies were identified as the K4L2E165YD, a 12 Gb LPDDR5X-9600 device fabricated on Samsung’s 1a node, the fourth generation of its 10 nm-class DRAM after 1x, 1y and 1z. 1a has shipped in volume since 2022, so this is current memory rather than older-node inventory. The 16 GB Pro Max variants we obtained were found with both CXMT and Samsung packages. The CXMT package is marked CXDD7JEDM, with two stacks of four dies, packaged in week 45 of 2025. The inferred die dimensions from X-ray computed tomography (CT) are consistent with a known density of ~0.3 Gib/mm² for the CXMT G4 process, roughly equivalent to other manufacturers’ 1z processes. The Kirin 9030 uses a typical integrated package-on-package (iPoP) stack: multiple DRAM dies in a memory package sit above an organic redistribution layer (RDL) interposer, which sits above the SoC and package substrate. The full package is then mounted to the printed circuit board (PCB) through ball-grid array (BGA) solder bumps. The memory package substrate is a thin bismaleimide-triazine (BT) laminate carrying the LPDDR5X stack. The organic RDL interposer over the SoC routes the PoP signals around the die and carries possible dummy thermal copper pillars. The package substrate, a thicker Ajinomoto Build-up Film (ABF) build-up over a BT core, fans the flip-chip bumps out to BGA pitch and embeds the power planes. The whole stack is organic. The only silicon is the SoC and the LPDDR5X dies; there is no silicon interposer. Keeping it all-organic brings the package’s coefficient of thermal expansion (CTE) close to the PCB’s, reducing board-level warpage, and avoids the cost of a silicon interposer the SoC’s bandwidth does not need. In an iPoP stack, the memory package connects to the organic RDL interposer through an array of solder bumps. Underfill fills the gap around those bumps, adding stiffness and protecting the joints from mechanical stress. The Pro and Pro Max variants differ here, which we cover behind the paywall. The die shot and architecture tell us how Huawei allocated its silicon budget. The process tells us what SMIC can manufacture. We use the Helio G99 as the process reference for TSMC N6. Both SMIC N+3 and TSMC N6 are evolutions of previous 7 nm-class nodes. We used targeted TEM cross-sections through logic and memory regions, imaged in both fin-cut and gate-cut directions. Each cross-section caption gives its horizontal field width (HFW), the real width of the imaged area. We start at the transistor fins, then move up through standard cells, local interconnect, and SRAM. SMIC has not overtaken Intel or TSMC. It uses aggressive DUV scaling and DTCO to reach N6-class density, but that density doesn’t translate into comparable performance and efficiency, for two reasons: the node gap to leading-edge nodes, and Huawei’s core designs. Fin Profile One of the most important knobs in a FinFET process is the fin profile: the shape of an individual fin and the channel where current passes from source to drain. The ideal fin is tall, narrow, and nearly vertical. A taller fin increases effective channel width, while a narrower fin improves electrostatic control by thinning the body the gate must control. Push either too far, and the process pays for it: weaker drive current, fragile fins, taper, footing and line-edge variation that hit yield and device variability. The Intel 22 nm, 14 nm, and 10 nm fin cross-sections show how FinFET nodes have improved over time. 22 nm fins were a first-generation structure, relatively short, wide and strongly tapered. The shape limits current density and reduces gate control uniformity across the height of the fin. At 14 nm and 10 nm, Intel pushed the fins taller and narrower while also making the sidewalls more vertical. Rather than shrinking the device, these changes increase the effective channel width per fin and improve electrostatic control. The trade-off is that taller fins at tighter pitches are much more difficult to manufacture. Now, let’s compare the Helio G99 on TSMC N6 with the Kirin 9030 on SMIC N+3. Both processes are in the same class, with fin pitch of 30-32 nm on N+3 and 34 nm in our N6 cross-section. The pitch for N6 is especially interesting as N7’s HD library is generally listed with a 33 nm fin pitch, and N6 did not shrink pitches directly. Its density gains came from DTCO instead of tighter pitches. The 34 nm pitch was stable across our sampled region and serves more as a comparison against the SMIC N+3 we have not investigated further. Pinning down N+3’s fin patterning scheme takes more than one core unit. The CPU cores show a dense ~32 nm pitch, with the pitch between N-P fin pairs alternating between 78 and 88 nm. Logic alone may be consistent with dual-pitch mandrels of 120 and 110 nm, but this is a complex and unusual approach. Combining the pitch from the the 8T SRAM, which has more complex repeat unit, with the CPU core sequence allows us to reverse engineer the patterning steps with more confidence. As both the logic and SRAM should share the same base grid, a single CD mandrel lithography pattern with 128 nm pitch undergoing SAQP produces a die-wide ~32 nm grid (128 nm/4), which supports the pitch sequencing seen in both logic and SRAM cells. In the sampled cross-sections, N+3 shows a taller, narrower, higher-aspect-ratio fin than N6. The measured fin aspect ratio is ~9.5:1 on N+3 versus 7.8:1 on N6. N+3 also shows less top rounding, with an estimated radius of ~2 nm, compared with 2.8 nm on N6. Even though the fin widths differ, the ratio of top rounding to fin width tells the same story, with N+3 at 0.37 and N6 at 0.44. In a geometric sense, lower is better; a perfectly rectangular fin would have no top-rounding penalty. These are single-digit-nanometer features measured from a handful of cuts, so treat the absolute numbers as approximate. The important result is the relative gap: N+3’s fins are consistently taller, narrower and less rounded than N6’s. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. Standard Cell A standard cell is the basic building block of chip layout: a fixed-height row pairing one NMOS and one PMOS transistor that share a gate, tiled in a grid to build logic blocks. The key dimensions are contacted gate pitch (CGP), cell height (CH), fin count, and the lower-metal routing grid. To measure density, we use the Bohr metric: a weighted average of NAND2 gate area (60%) and scan flip-flop area (40%). This represents a realistic mix of combinational and sequential logic. This metric has its limitations, especially for complex cell layouts like TSMC’s FinFLEX, which alternates cells with different fin counts. Even so, it is the best metric for a pure process-level comparison. Another important measurement is the fin pitch; it refers to the distance between two fins of the same transistor. In a FinFET process, multiple fins are used in each transistor to increase the drive current and thus performance. TSMC N6 ships both a high-density (HD) library with 2 PMOS and 2 NMOS fins per cell, and a high-performance (HP) library with 3 of each. More fins under the shared gate mean more effective channel width. HP cells switch harder at the cost of area. Designers mix the two on a die, primarily spending HP cells on timing-critical paths, and matching their PPA targets. In the Cortex-A55 core of the Helio G99, we found a cell height of 240 nm for the HD cell. MediaTek has used HD cells in the G99 to minimize die size and thus cost. As an SoC for budget smartphones at ~$100, this is essential. By contrast, we found only one library in the Kirin 9030, with 2 NMOS and 2 PMOS fins. This suggests a narrower library strategy than TSMC N6, where both HD and HP libraries are widely used. This likely reflects the smaller customer base and the more constrained domestic design and electronic design automation (EDA) ecosystem. In all three CPU cores of the Kirin 9030, we found cell heights of 228 nm, 5% smaller than on N6. This is also a reduction of 9.5% over SMIC N+2’s cell height of 252 nm. SMIC N+3 and TSMC N6’s HD library both feature a CGP of 57 nm. For SMIC, this is a 9.5% shrink over N+2. In the past, CGP and cell height alone may have been enough to compare transistor density. Now, however, we must consider scaling boosters and DTCO as well. SMIC’s density gain does not come from EUV. It comes from using every available DTCO booster aggressively. First is fin depopulation: reducing the number of NMOS and PMOS fins in each cell. The first FinFET nodes started with 3 or 4 fins for each transistor. SMIC N+3 and TSMC N6 HD both use only 2 fins per transistor, trading drive strength for density. Next is contact over active gate (COAG). By landing the gate contact directly over the active gate, instead of out over the isolation region, the cell height drops. N+3 integrates COAG while N6 does not. Our N+3 gate-cut cross-sections indicate COAG, with the gate contact sitting over the active region, while N6 shows an off-gate contact. Last is single diffusion break (SDB). Diffusion breaks are inserted between cells in the same row to provide electrical isolation, but they also introduce local layout effects (LLE), layout-dependent shifts in electrical characteristics. In the past, a double diffusion break was used, consuming the space of two CGPs. SMIC N+3 and TSMC N6 instead use SDB, saving area but increasing LLE sensitivity. This must be controlled at the process level and accurately modeled in the process design kit (PDK) so that EDA tools can account for it. Overall, SMIC N+3 has a transistor density of 113.4 MTr/mm², slightly above TSMC N6 at 107.7 MTr/mm². Even without EUV, SMIC has achieved density beyond TSMC’s mature N6 node which utilizes EUV. Metal Stack The smallest critical dimension in the teardown is M0; SMIC N+3 uses a 32.5 nm local metal pitch. That is smaller than the 36 nm M0 pitch on Intel 18A in Panther Lake. However, this does not mean that SMIC has a better process than Intel 18A or TSMC N3P. M0 is a local intra-cell routing layer. Its usefulness depends on the full interconnect stack: M1 and M2 pitch, track count, via and line resistance, design rules, mask count, overlay control, and routing flexibility. The 32.5 nm M0 is consistent with self-aligned quadruple patterning (SAQP), whose four-population line-width loading we read coarsely as alternating widths of 21.5 to 24 nm; M1 and M2, at 38 and 40 nm, are consistent with self-aligned double patterning (SADP), a single A/B split. On TSMC N6, M0, M2, and M3 sit at a relaxed ~40 nm and are consistent with SADP-class double patterning, with no need for quadruple patterning. That said, we measure M2 for example at ~43 nm, likely inflated by sparse routing. We do not assign any specific layer to EUV from our cross-sections; the distinction we can draw is double versus quadruple patterning, not lithography wavelength. Transistor-level density in the front-end-of-line (FEOL) sets an upper bound, but the design is ultimately limited by what the interconnect stack can route. The lowest metals are the most important for standard-cell density, but the semi-global and global layers determine how usable that density is at the block and chip level. Two axes are commonly used for chip cross-sections: the fin-cut and the gate-cut. The micrograph above is a fin-cut and shows metals 0 through 3. This axis lets us see the even-numbered metals, with M0 right above the fins. There are two kinds of M0 lines. The first are the power rails; these are wide wires for the VDD and VSS running horizontally at the top and bottom edges of each standard cell. The wide wires measure 55 nm across, more than double the other M0 lines. Their width minimizes resistance and reduces IR drop. The second kind are intra-cell wires, short segments within the cell that connect terminals to M1. These have alternating widths between 21.5 and 24 nm. The M0 pitch is 32.5 nm, a 19% reduction versus N+2 and N6. At this pitch, DUV patterning requires more aggressive multi-patterning, increasing mask count, overlay sensitivity, process complexity and cost. M0 is below what a single DUV-defined spacer (SADP) can resolve, so SMIC cascades a second spacer step (SAQP). The cross-section reflects the cost: the M0 trenches are visibly more re-entrant (narrower at the bottom than the top) than M1 or M2 on the same chip and carry a bright barrier-rich foot where the trench meets the etch-stop layer. That shape is partly the intended damascene profile, as a slightly narrow bottom helps void-free copper fill, but its magnitude at M0 is driven by the tight pitch and the higher trench aspect ratio. Intel 18A supports an M0 pitch of 32 nm, although Panther Lake has only shipped with a looser 36 nm pitch. This is due to Intel’s heavy usage of HP libraries. Among leading-edge nodes, 18A has the loosest M0 pitches due to PowerVia. With power routing moving to the backside, congestion is reduced, and the entire front-side metal stack can be used for signal routing. M2 is the first true inter-cell routing layer. It runs horizontally like M0 but spans across multiple cells to carry block-level signals. The M2 pitch sets the cell’s track height – the number of M2 tracks that fit between the VDD and VSS rails, defining what the library calls a 6-track or 7.5-track cell. This layer is the most important, limiting the routing of entire blocks. SMIC N+3 features a 5.7-track cell. The M2 pitch is 40 nm, a 5% decrease over N+2 and the same as N6. This shrink keeps the pitch at the edge of what is possible with double patterning. Future nodes will need to increase the number of masks for M2 as reducing the number of tracks is much harder due to the limitations in routing. The micrograph above is in the perpendicular direction, the gate-cut, and shows metals 0 through 4. This allows us to see and measure the odd-numbered vertical metal layers. The M1 pitch is 38 nm, 9.5% less than N+2 and 33% less than N6. The M1-to-gate ratio matters because it sets local routing flexibility. N+2 and N+3 use a 3:2 ratio, while N6 uses a 1:1 ratio, explaining the huge differences in M1 pitch. The more M1 lines there are compared with the gates, the more flexibility there is for power and signal crossing within the cell. Routing flexibility enables more complex and better cells. Clean fractional ratios are also preferred as a grid is periodic and improves layouts. The 3:2 ratio gives SMIC more local routing flexibility than a strict 1:1 grid, but it also complicates layout and patterning. This is a DTCO choice, with SMIC increasing process complexity to recover density and routability without EUV. This 3:2 ratio is not very popular in the leading-edge nodes. TSMC has only used it on N7+, the N5 family, and the short-lived N3(B). They have switched back to a 1:1 ratio for N3E. Intel has only used it on the 10 nm/Intel 7 family, with Intel 4, 3 and 18A all using a 1:1 ratio. Samsung is the only one still using a 3:2 ratio at the leading edge, using it in the SF4 and SF3 families. It remains to be seen if SMIC will remain at a 3:2 ratio or move to a 1:1 ratio with its future nodes. The industry is still actively exploring these local-routing ratios. At VLSI 2026, imec will be presenting work on even higher ratios, including a 2:1 scheme that can reduce area by up to 14%. We will be covering the conference in a future newsletter article. Subscribe We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Interested in joining us on this ride and think you can be a difference maker? Check out our Careers page. The final local interconnect layer for N+3 is M3, with a pitch of 44 nm. The M3 pitch is the same as on N+2 and 10% larger than on N6. The semi-global layers carry the majority of block-level signal routing. They have a coarser pitch than the lower local layers. On leading-edge nodes, they are designed to sit at the limit of DUV single patterning. M4 through M11 pitches were found divided between 80–82 nm (M4–M6), 128 nm (M7–M10), and 148 nm (M11). Given limited sampling, it is possible these are divided further in dense routing areas. At the top are two giant metal layers, M12 and M13. These have kept the same pitches as N+2 at 1920 nm and 4600 nm respectively. While the lower layers’ pitches are generally fixed by the process and library, the upper layers vary much more in pitch and count, depending on the design. Even two smartphone SoCs on the same process can have wildly different metal stacks. The Helio G99 carries fewer routing layers, reaching coarse metal pitches of 850 nm by M9, while the larger and higher-performance Kirin 9030 keeps fine pitches until M11. SRAM At the leading edge, SRAM is much more difficult to scale than logic. TSMC’s latest nodes have seen little to no bitcell scaling, while logic still has more DTCO levers to pull. While looking for other logic libraries in the GPU compute units, we stumbled upon the SRAM. The most common type of SRAM has 6 transistors (6T), but this cell had 8 transistors (8T) instead. 8T SRAM adds two transistors to form a dedicated read port. Unlike a 6T cell, where reading disturbs the storage, the decoupled read port removes read-disturb, improving read stability and letting the cell be pushed harder for performance. At first glance, the cut looked like an unusual logic library, with each cell row having 3 fins of one polarity and 5 fins of another. The rows also alternated in orientation. Energy-dispersive X-ray spectroscopy (EDS) resolved our confusion. The cut had not landed on the GPU logic, but on the SRAM macro beside it. The unusual fin pattern was due to the SRAM library. We return to EDS in the process flow analysis behind the paywall. SRAM libraries are not like traditional logic libraries. Due to the unequal number of PMOS and NMOS transistors, they require specialized rules and layout libraries. They do not need the flexibility of logic libraries, so they are hyper-optimized for one purpose: dense, reliable memory. The SRAM cell we found is a 1:2:2-2:2 cell. This means there is 1 fin per pull-up (PU) PMOS transistor, and 2 fins per pull-down (PD) and pass-gate (PG) NMOS transistor. These 2 PU, 2 PD and 2 PG transistors would usually form a single 6T high-current cell (HCC). An 8T HCC adds a read-pull-down (RPD) and a read-pass-gate (RPG) NMOS transistor, each with two fins. We measured a cell height of 406 nm, which brings the bitcell size to 0.0463 µm². That is a theoretical peak density of 21.6 Mib/mm². We estimate that a 6T HCC would have a cell height of 292 nm and a size of 0.0337 µm². This is ~12% larger than a 6T HCC on Intel 3 and 4. We also estimate the 6T high-density cell (HDC) to have a cell height of 228 nm and a size of 0.0260 µm². This is coincidentally the same as the logic standard-cell height measured earlier. The estimate puts the cell near Samsung 7LPP/5LPP and slightly below TSMC N7/N6. That is a theoretical peak density of 38.5 Mib/mm². 6T HDC is arguably the most important cell as it is used for the largest caches in a chip, the L3 caches and system-level cache (SLC). Both the Kirin 9020 and 9030 have split the SLC into 4 banks to raise total SLC bandwidth. In the Kirin 9030, the SLC increased from 2 MiB to 3 MiB per bank. Correspondingly, the number of arrays within the bank also increased by 50%, from 16 to 24. Each array can store 128 KiB and forms an orderly pattern on the die shot. From the Kirin 9020 to the Kirin 9030, the area of a 128 KiB SLC array decreased from 0.0477 mm² to 0.0392 mm², an 18% shrink. The achieved density is 25.5 Mib/mm², 66% of the theoretical maximum. While the SLC was quite similar across both chips, the L3 has seen some major changes, particularly in terms of its layout. The total capacity also went up from 10 MiB to 12 MiB. Much like the SLC, the L3 is also split into 4 banks. In the Kirin 9020, an L3 bank consisted of 16× 128 KiB arrays and 16× 32 KiB arrays. However, an L3 bank in the Kirin 9030 instead consists of 48× 64 KiB arrays. In the Kirin 9020 L3, a 128 KiB array was 0.0513 mm² and a 32 KiB array was 0.0154 mm². The size of the 128 KiB array is different on the L3 and SLC as the assist circuitry for the two arrays differs depending on their purpose. In the Kirin 9030 L3, a 64 KiB array is 0.0210 mm². Although not a like-for-like comparison, normalized for capacity, it is 18% smaller than the 9020’s 128 KiB L3 array and 31% smaller than its 32 KiB L3 array. The achieved density is slightly lower than the SLC, at 23.8 Mib/mm², 62% of the theoretical maximum. Unlike the L3 and SLC, the prime cores’ private L2 cache uses a 2-bank design. As the prime cores’ L2 is latency-critical, it likely uses 6T HCC instead of 6T HDC. The 9020 has 16 arrays in each bank while the 9030 has 32. Each array has a capacity of 32 KiB. A 32 KiB array in the L2 shrank from 0.0171 mm² to 0.0142 mm², ~17% smaller. The density is 17.6 Mib/mm², ~59% of the theoretical maximum for 6T HCC. SRAM scaled well from N+2 to N+3, shrinking by ~19%, close to the theoretical logic shrink. The caveat is that N+2’s bitcells were unusually large, bigger than comparable 7 nm-class nodes, so part of the gain is catch-up rather than true scaling. With the insights from STEEL’s teardowns, we will be doing a deep dive into SRAM in a future newsletter article. Subscribe Everything above came out of a single STEEL teardown: die annotation, block-level area analysis and TEM cross-sections through logic and SRAM. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline or to commission a custom teardown, contact sales@semianalysis.com. Future Roadmap The same cross-sections that pin down N+3 also show where SMIC can go next. Although N+3 is already close to the practical limits of DUV multi-patterning in several layers, SMIC still has a few scaling levers left. A theoretical N+4 would likely start with cell height. N+3 uses 5 M0 tracks between its power rails. Moving to 4 M0 tracks, as on SMIC N+2 and TSMC N6, could reduce cell height by roughly 15%. The routing grid is only one side of the shrink; the front end also must fit into the smaller cell. One possible FEOL lever is reducing the p-to-n isolation spacing from two diffusion grid units to one. Intel used this scaling booster on Intel 4, and TSMC did so on its N3 family. This path trades layout flexibility for density. Fewer M0 tracks reduce local routing resources, while tighter p-to-n spacing raises integration and design-rule difficulty. M2 is also constrained by the cell height shrink. For SMIC to maintain a ~5.7-track cell, M2 would need to move toward ~35 nm. That would move another layer into SAQP territory. SMIC could also reduce the CGP from 57 nm to 54 nm. Intel reached a similar CGP on Intel 10 nm/Intel 7 without EUV. The local interconnect is also tougher. If SMIC keeps the 3:2 M1-to-gate ratio, M1 would need to shrink to 36 nm and would likely require SAQP as well. If SMIC moves to a 1:1 ratio, M1 could relax to 54 nm, but it would give up routing flexibility. Under this theoretical path, we estimate that SMIC N+4 could reach a cell height of 198 nm and a CGP of 54 nm, implying a Bohr density of 137.8 MTr/mm², on par with TSMC N5 or Samsung SF4. However, the difficulty is cumulative. Each step is individually plausible, but together they make N+4 harder than the transition from N+2 to N+3. It will likely take longer, cost more, and carry less process margin. A theoretical N+5 would require a larger integration shift. One possible path is backside contacts (BSCon), moving power routing and source/drain contacts to the backside, which would reduce front-side routing pressure and enable another reduction in cell height. The front-side metal pitches could relax to reduce the process complexity. M0 would likely relax slightly to ~34 nm, and M2 and M4 pitches could relax further. CGP is unlikely to shrink much further. Even with EUV, 48 nm has been the practical limit for yield and process control. This approach would allow N+5’s cell height to fall to 170 nm and its CGP to 53 nm. This implies a Bohr density of 163.6 MTr/mm², on par with Intel 18A’s HP library. However, this would not make N+5 cost-competitive with the leading edge. It would reach a similar density through a much more expensive route. The integration difficulty rises sharply, with new process flows for backside alignment, wafer thinning, contact reveal, and backside metallization. Past this point, standard density and interconnect scaling become increasingly unattractive. That is where Huawei’s roadmap stops looking like a normal foundry roadmap and starts looking like a packaging roadmap. At ISCAS 2026, Huawei unveiled its tau (τ) scaling law, reframing process scaling in the time domain. τ is the time cost of data movement and processing: switching delays in transistors, RC signal propagation delays in circuits, compute, memory, and networking latency. Outside Huawei’s terminology, this is called system-technology co-optimization. This is Huawei’s answer to its lack of EUV lithography. Without EUV, planar density cannot keep pace with TSMC, Intel, or Samsung. If transistor density cannot shrink further, Huawei’s alternative is to shorten wires, reduce buffering, and stack logic vertically. “LogicFolding”, Huawei’s implementation of this new scaling idea, is, in practice, an aggressive 3D stacking approach. AMD V-Cache places SRAM above or below a CPU die. AMD’s MI350X places active interposer dies (AIDs) underneath accelerator and compute dies (XCDs), with the AIDs handling cache, IO interfaces, the network-on-chip (NoC) and embedded metal-insulator-metal (MIM) capacitors. With LogicFolding, parts of the same logic block are split across multiple active dies bonded face-to-face at ultra-fine pitch. This allows Huawei to shorten some critical paths and reduce buffer overhead, not merely add cache capacity or offload the IO and interconnect. Shortening wires is where the higher clocks come from. A large share of a modern core’s delay and energy budget goes into driving long interconnects and the repeater buffers along them. LogicFolding distributes a block’s critical-path gates across multiple stacked tiers bonded at a very fine pitch, so the bond interface behaves like an additional metal layer and the longest paths get shorter. That is how Huawei expects to recover frequency and efficiency it cannot get from the process alone. Huawei’s roadmap shows its intent. Prime core frequency is targeted to rise from 2.75 GHz in the Kirin 9030 to roughly 5 GHz by 2031, far beyond what planar scaling alone could deliver. Prime cores with 3.1 and 3.39 GHz clocks are being tested in its labs, although their power consumption is unknown. Beyond that, chips are in the design, simulation or pathfinding phase, meaning the frequencies are targets. However, the direction matters more: LogicFolding also helps with performance, not just density. The catch is that Huawei’s density claim is not directly comparable to foundry densities. A stacked design can report more transistors per package footprint by adding active layers, even if each patterned die remains well behind TSMC or Intel in front-end density. This is how Huawei can claim to reach foundry 14A-equivalent density by 2031. This is not a like-for-like foundry comparison, with Huawei using stacked logic and measuring density per package footprint. On a normalized Bohr-density basis, SMIC N+3 is ~114 MTr/mm², 38% less than Intel 18A’s HD library. Huawei’s 3D roadmap closes the gap by stacking active logic, reaching 215 MTr/mm² by 2030. In 2031, the roadmap density jumps to 295 MTr/mm², implying either a third active layer, partial EUV insertion or aggressive planar DUV scaling. Huawei’s methodology makes other foundries look much denser as well. Applying it to AMD’s MI450X with an N2 top die on an N3P base die yields a theoretical density of 460.2 MTr/mm² in 2026, compared with Huawei’s 295 MTr/mm² in 2031. This Kirin 9030 does not use LogicFolding, remaining in a conventional mobile SoC package. Instead, it forms the baseline for how far Huawei and SMIC can push planar scaling. Future teardowns of Kirin and Ascend chips will show both planar logic density and Huawei’s hybrid bonding solutions. Export controls changed China’s optimization problem rather than ending it. EUV restrictions raised the cost and complexity of leading-edge manufacturing without freezing it. SMIC reaches N6-class logic density through DUV immersion, SAQP and DTCO, while Huawei shifts more of the burden onto architecture, packaging and system-level integration. Future nodes will be tougher. N+3 still had room to tighten local metals and reduce cell height and CGP. Further scaling without EUV leaves fewer levers. More aggressive multi-patterning adds masks and overlay error. SMIC can keep pushing DUV, but each step will get more expensive and less forgiving. The design side is just as critical. Huawei had domestic EDA tools and flows before the Kirin 9030, with the Kirin 9000s, 9010 and 9020 making that clear. Huawei was able to ship multiple consumer SoCs on SMIC N+2 and N+3 while cut off from the Western EDA stack. US export controls restricted EDA tools for advanced chips in 2022 but did not target tools for more mature chips. In 2025, the US government briefly placed much broader restrictions on EDA software from Synopsys, Cadence, and others, before lifting them less than two months later as part of a trade deal tied to rare earths. Huawei has been unable to access those tools because it remains on a US trade blacklist. That forced Huawei, SMIC and Chinese academic institutions to build their own tools and flows. Researchers at Peking University recently announced a prototype EDA tool for Huawei’s LogicFolding architecture, which requires a new flow to handle the multilayer layout and floorplan. This is not the same as replacing the full Synopsys or Cadence stack, but it shows where domestic EDA is headed: toward tighter co-optimization between architecture, process and packaging. These advances are also diffusing into the Chinese ecosystem. SMIC is licensing its N+2 and N+3 processes to HLMC/Hua Hong at the government’s direction rather than by choice. If the same process learning feeds into Ascend accelerators for AI training and inference, the choke point shifts from one named fab to an ecosystem. Alibaba’s T-Head silicon arm and Cambricon, a Chinese AI chip designer that is expected to supply ByteDance, could also be major beneficiaries. Sanctions aimed at SMIC alone become less effective once the manufacturing knowledge has spread to other fabs and design houses. China is not closing the gap with Intel, Samsung and TSMC. The teardown shows the opposite in several places: no EUV, no backside power, higher process complexity, and visible trade-offs. But China is still advancing. If domestic chips become good enough for phones, inference, networking and security-sensitive workloads, they can matter strategically without matching TSMC at the leading edge. Behind the paywall, we show what else STEEL can do, with material and process flow analysis of SMIC N+3, and analysis of the Kirin 9030 package. We’re diving deep into the most advanced datacenter and AI hardware hitting the market. To learn more about what’s in the pipeline, access the full Kirin 9030 and SMIC N+3 analysis or to commission a custom teardown, contact sales@semianalysis.com.
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We can create the future of science right now
Paul Litvak is the founder and Executive Director of the Robyn Dawes Institute and a Visiting Scholar at UC Berkeley. He has a PhD in Behavioral Decision Research from Carnegie Mellon where he studied emotions and the sunk cost bias. Over 15 years in industry, he solved a wide range of challenging technical problems. At Meta he created models optimizing the ad review process. At Google he ran experiments to measure social influence. At Airbnb, he was a product manager leading machine learning teams optimizing search ranking and price suggestions. He also co-founded and led product at Rhythmic Health, a venture-backed biosensing startup, creating an accurate low cost system using a color changing strip and a smart phone to measure salivary lactate. The bottleneck At this point, it is uncontroversial to say that science needs to stop using the PDF article as the unit of knowledge and currency. The unbearable slowness of scientific publishing, the profit motive and margins: I’m not saying anything new. The PDF also sucks because it’s hard to extract structured information, which makes it hard to do evidence synthesis. As a result, we do much less evidence synthesis than is needed. And evidence synthesis ultimately undergirds most policy and medical decision-making. I can see second-by-second real-time odds for any sporting or newsworthy event, but a school board can’t see the best evidence on whether their 8th graders should be taught algebra. As a society, we don’t treat this as an important problem. Again, not controversial. Not only is the problem well understood, but the solution has already been laid out. What we need is AI-assisted living evidence synthesis - (1) an open knowledge graph of atomic claims, (2) claims linked to evidence, (3) assessment and synthesis of each piece of evidence, and (4) continuous updating with new data. What few realize (yet) is that the technical capacity to build this vision for a significant portion of science already exists. Not only that, scientists and startup teams are already building many of these components. I know this because I’ve been surveying the space and talking to many of the builders. There are some missing pieces: for example, evaluations of how well some of the components work. But at this point, most of what’s missing is a fully end-to-end working integration of all of these parts. In the rest of this essay, I’m going to lay out all the parts of a working living evidence layer for science and who is working on them, and propose concrete next steps for building this system. What’s now possible The diagram above outlines the components of a living evidence synthesis platform, including some of the teams working on each component1. Scientific PDFs are processed into claims with associated evidence. The evidence is subjected to a forensic audit, methodological evaluation and robustness and reproducibility checks. Finally it’s given a weight in a continuously updating synthesis. What follows is a description and status of each component and a few of the teams working on them. Document understanding The first thing you need to be able to do is turn an article into structured data. Mostly that means parsing PDFs. There are often multicolumn layouts that confuse non-specialized PDF to text processing libraries. For scientific papers, there is the added complexity of parsing formulas and tables and figures. This is a really hot area - there are startups offering APIs, and it seems like a new open source package gets posted to Github every few weeks. What follows isn’t exhaustive. A package called GROBID was the state of the art for a while, they didn’t update their package for nearly two years until very recently. In the meantime reducto.ai released an AI powered PDF extraction API, PaddleOCR became popular, IBM released a model called Docling, and both Mistral and Gemini created models and libraries. I also know of at least one other well-funded psychology research group working on a paper parser. By contrast, there are few open evals in this space, with no extensive evals for complex table comprehension in particular. Nonetheless, I’m confident this will be a solved problem soon, given the combination of LLM advances and developer interest. Hypothesis level extraction There has been increasing interest in comprehending the extracted text of papers and linking information to evidence for each hypothesis. A lot of work has already been done. Trialstreamer (Marshall et al. 2020) and RobotReviewer LIVE (Marshall et al. 2023) demonstrated automated extraction of trial population, intervention, and outcome at scale on clinical RCTs. PaperQA2 (Skarlinski et al. 2024) and Ai2 ScholarQA (2024) extended this to retrieval-augmented question answering with citation grounding. Elicit, Consensus, and SciSpace operationalized claim-level extraction for end users. OpenEval (Booeshaghi et al. 2026) is the most recent and most ambitious: 1.96 million atomic claims extracted from 16,087 eLife manuscripts using Claude Sonnet 4.5, grouped into ~299,000 results, with LLM evaluations showing 81% agreement with human peer review on a 2,487-paper subset. None of these solutions link claims to test-statistics, as you would need to evaluate randomized controlled trials. This is why I built the evidence.guide API - to extract hypotheses and associated test statistics from behavioral science papers. The best public eval of this kind of extraction I’m aware of comes from the recent SCORE project - they had humans code thousands of psychology papers to extract their claims by hand. It would be extremely helpful to the world if all scientific PDFs were available as structured open data. I’ve been working to make this happen, both directly at Berkeley and through coordination with large entities I can’t yet speak of; as hard as it is to do, I think it’s possible2. Forensic audit A lot of work has been done on forensic audit, but some gaps remain. Of course, for biology papers that rely on images for evidence, there are a variety of tools (notably Proofig and ImageTwin) to spot anomalies. These are still well short of what sleuths like Elizabeth Bik can do on her own, but these tools are constant companions among fraud analysts. There’s someone working on auditing Excel files for anomalies, and a number of teams are automating numerical checks like GRIM and SPRITE, including Scrutiny project, the INSPECT-SR team as well as statcheck. The regcheck team is building a way to use AI to compare preregistrations to analyses in papers, to ensure there aren’t significant deviations. Nonetheless, there are many other kinds of anomalies to screen for, both public and less publicly known. And there are no formal evals for anomaly detection that I’m aware of. Still, there’s a lot to draw from in this space and I’m pretty certain we will be able to scan papers for most kinds of obvious anomalies in the near future. Methodological review This area has been white hot, though I fear for many of the startups in this space, because this capability may become commoditized. There are at least six different AI peer review companies, including Refine.ink, Reviewer3, ReviewerZero.ai, Q.E.D. Science, Paper Wizard, and Isitcredible. Coarse (a pun on refine) was also recently created as an open source alternative. These systems provide qualitative feedback on the content of papers, spotting methodological weaknesses and mathematical errors. They seem to work pretty well, and many academics report bitterly that they exceed the average quality of typical peer reviewers. But there are few evals here either. What evals exist so far involve using LLM-as-judge (circularity problems abound) or comparing against human reviews of questionable quality. What you’d ideally want is an eval that measures capturing known errors in papers3. Reproducibility and robustness Another active area has been using AI agents to automate computational reproducibility4 and robustness5 checks in papers that report numerical results. For more recent papers where data and code are available, AI agents can see whether they can re-run the analyses and produce the numbers reported in the published paper. In addition to a handful of individual academics who have been experimenting using Claude Code for this, the Institute for Replication is a leading group working on building an end to end system. The evals related to this problem are the most mature, with CORE-Bench (Siegel, Kapoor, Narayanan 2024) and PaperBench (OpenAI 2025) available to benchmark agents on this task. There is also work on getting AI agents to test alternative ways of analyzing the data to ensure the results are robust to small analytic design choices. Synthesis This is the most underdeveloped area where significant investment is required. Although some automated evidence synthesis systems exist — for example, otto-sr is building an AI agent to write systematic reviews — none of these incorporate the full range of paper level signals to weight evidence appropriately. Nor is there anything like an eval or a gold standard for a good systematic review. Arguably Cochrane reviews are the closest we have to gold standard human systematic reviews, though I’ve heard academics in the know complain about their uneven quality. A key question for a synthesis platform is how to weight anomalies and methodological issues in assessing the quality of a piece of evidence. This is an unsolved problem and one I’m very keen to work on. Continuous updating There are many pieces of basic infrastructure available for monitoring for new research and initiating updates. OpenAlex is the current open citation graph. Retraction Watch integrated into Crossref in October 2023. Scite tracks how citations support, contrast, or mention prior claims. The Living Evidence Network demonstrated continuous-update workflows in clinical guidelines. Engineering this is a relatively straightforward task. When you look over this technical architecture and all the progress being made, it’s hard not to be optimistic that a living guide to scientific evidence will be built. The stakeholders are ready The social infrastructure for this is starting to coalesce — it’s not just a pie in the sky academic exercise to imagine this coming into existence. Institutions like the Center for Open Science, the Institute for Replication, the INSPECT-SR, the Living Evidence Network and more are all working on scaling work to improve research quality. Funders are also aligned. The Sloan Foundation has funded living evidence work through COS. Coefficient Giving supports the Institute for Replication and COS. The Astera Institute and the Institute for Progress have shown interest in this space. NIH has established an Office for Replication and Reproducibility. Although there are (very unfortunately) serious headwinds in science funding generally, there is an active group of funders interested in metascience. A brief word about what I’ve been doing at RDI. First, as a Visiting Scholar at Berkeley I’ve been actively figuring out how a non-profit and a public university can conduct and make public the results of large-scale academic article data mining. With some of the money I raised from donors, I commissioned a legal analysis of recent case law and publisher text data mining (TDM) agreements in order to understand whether a massive open data mining of academic articles is possible (with caveats, it is). I’ve also been working to bring together stakeholders in this space, and identify gaps. I’ve also been doing some software development in this space, with more to come. A pilot proposal The assumption undergirding all of this is that an AI, given all this information, would make the right judgment about a scientific claim with lots of conflicting evidence, weighing all the factors appropriately. That’s the hypothesis we need to test. Randomized control trial research is the best place to focus on first. RCTs are used to make many of the important decisions in society - from medical trials to public policy changes. And they use a relatively uniform set of inferential statistics with lots of known and available diagnostics. Behavioral science experiments, within the broader realm of RCTs, should be first used as a testbed whose results can be generalized. Because behavioral science is at the vanguard of open science practices, replications abound (there are thousands of them) to serve as ground truth training data. Key Hypothesis Therefore the pilot would test, in behavioral RCTs that have been replicated, whether the quality of evidence for a claim can be used to accurately predict whether that claim will replicate. Secondary Hypotheses Compared to claims that replicated, non-replicated claims demonstrate a greater share of forensic anomalies in their source literatures. Hypothesis level claim and statistic extraction is accurate enough to scale living evidence without onerous human review costs. Replication prediction is more accurate than prediction markets6 or journal prestige. If all the different quality signals we gather do accurately predict which studies will replicate, then we can use that model to score evidence to power the living evidence layer. Why this is informative regardless of outcome If the pilot succeeds, the architecture extends to medical RCTs (where Living Evidence already operates and integration is mostly about claim representation), then to slices of basic biology with stable replication structure. If it fails, the field learns which quality signals are load-bearing and which ones metascience has oversold. Either result is a contribution to knowing what the literature supports. Implications for funders Because this burgeoning ecosystem of builders already exists, a well-informed philanthropic or government funder could play a crucial catalyzing role in bringing this future about. They could play at least three roles: creating open structured datasets, publishing open benchmarks and incentivizing the solving of key technical challenges. First, open archives of papers that the government maintains, like PubMed, could be turned into structured data amenable to large scale metascience and claim aggregation. I know the US government already has an interest in doing this, though some key open questions remain unanswered. How do you determine the best models and systems for accurately extracting information from papers? How can you establish a robust way to allow researchers to flag errors and correct them? And finally, how do you create a legal regime in working with publishers to maximize the scope of available papers? For the latter, a university or a private philanthropy may be better positioned to make structured data publicly available under journal subscription terms or fair use. Philanthropists or government funders could also coordinate to create or commission benchmarks that evaluate whether important problems have been solved. For example, an open benchmark for claim extraction from a range of different scientific article types would be extremely helpful. Ensuring the underlying data are accurate is vital for creating these evaluations. I’ve discussed opportunistically using various human-created datasets for this purpose, but a consistent problem is that errors in human data make it difficult for them to serve as a gold standard. Finally, with structured data and benchmarks available, the government or private philanthropy could use them to incentivize groups to develop machine learning systems that meet these benchmarks. Prizes are one potentially valuable tool for this. For example, you could establish a prize for a system that accurately updates a living meta-analysis for a small set of claims. Prizes are particularly useful as signals of problem importance, and can help create vibrant ecosystems of public and private research—see, for example the role the government played in kickstarting the current work on nuclear fusion. Conclusion The drawbacks of the current scientific publishing system are known. Scientists agree, metascientists agree, philanthropists agree: the published PDF plus citation graph isn’t the right substrate for maintaining a representation of the evidence base in science. The pieces needed to build the alternative either already exist or are rapidly taking shape. The community is forming around exactly this problem, with concrete partnerships and shared infrastructure. A pilot should start on behavioral science RCTs because that’s the slice of empirical science most amenable to legibility, where replication ground truth is richest, and where the failure modes are best documented. What’s been missing is the galvanizing mission to assemble these pieces into something that works. That’s what I’m proposing to build. *** 1 I have a broader field map that I’ll release publicly soon. This is me, building in public! 2 If this is something that you are excited about, please reach out and talk to me. 3 More on this very soon too! 4 This tests whether, given the code and the data, you can get the same statistics as reported in the published paper. 5 This tests whether the results are the same as a paper’s given alternative analytical decisions in conducting the analysis (like outlier omission). Closely related is the idea of a “multiverse” where you come up with many different ways of answering the same underlying research question with the same data, and test whether the results hold in all those alternative methods. There’s been work on the latter as well. 6 Some of the replications, e.g. those from the SCORE project, had paired the experiments with forecasts from prediction markets. So we get to look at this for free.
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How integrating tradition and self-care has strengthened public health in the Americas
On 13 May 2026, a regional dialogue for the Americas titled, Innovative Experiences to Strengthen Health and Self-Care through Traditional, Complementary, and Integrative Medicine, was hosted by EsSalud, the government agency responsible for social security and health care in Peru.The event brought together representatives from the World Health Organization (WHO), the Pan American Health Organization (PAHO), WHO collaborating centres, the health sector and academia. It presented regional perspectives from BrBolivia, Brazil, Mexico and Peru to explore how self-care and traditional medicine can strengthen public health, empower communities and support more inclusive models of care. Across all four countries, a clear message emerged: traditional medicine is not being positioned as an alternative to formal systems, but as a way to strengthen them. In the Americas region, the challenge is to consolidate intercultural and holistic care, with the aim of strengthening the healthcare system – making care more preventive, culturally relevant and community-based.Peru: institutionalizing complementary care over 28 years Dr Martha Villar López from EsSalud Peru demonstrated how integration can be institutionalized over time. With nearly three decades of investment, including a research programme dating back to 1998, traditional medicine is now embedded within public health infrastructure. EsSalud’s flagship programme, Reforma de Vida, operating as Prevenir, takes multidisciplinary teams into workplaces to reduce and detect risk factors for chronic noncommunicable diseases. Education cycles are structured around three pillars: celestial nourishment, focused on life purpose and movement; terrestrial nourishment, centred on regional food and healthy diets; and human nourishment, addressing identity, relationships, stress and spirituality. Grounded in Andean cosmovision and the principle of the ayllu, the programme aligns with local cultural values. Since 2007, it has trained over 6600 health leaders, certified more than 3100 health-friendly organizations and built alliances with nearly 11 000 employers.Bolivia: legal recognition and medical pluralism Adelino Pau Carpacheco, an 82-year-old Callawaya healer, described working with more than 220 medicinal plants, supported by deep generational knowledge. Traditional healers and plant medicine are highly respected in the country. Bolivia’s contribution also highlighted the role of policy in enabling integration. Through Law 459, traditional medicine has been formally recognized within a legal framework and led the country to medical pluralism where traditional medicine coexists with biomedicine, and both are now offered free of charge through the public health system. Mexico: preserving Indigenous knowledge and reinventing food culture Mexico presented two complementary approaches focused on knowledge preservation and food systems. In Veracruz, communities partnered with a university to safeguard Totonac traditional medicine through a diploma programme launched in 2019. This collaboration has already produced practical outputs, including manuals and a dictionary, helping to sustain knowledge at risk due to migration and generational change. Alongside this, Dr Hernán García, Directorate of Traditional Medicine and Intercultural Development – Mexico, explained how the Dieta de la Milpa programme addresses Mexico’s dual burden of malnutrition and rising noncommunicable diseases. Rooted in traditional Mayan cultivation systems and ingredients, it promotes healthier diets through community-led, intercultural approaches involving local authorities, Indigenous leaders and cooks. By reconnecting food, culture and health, it offers a practical pathway to prevention. Brazil: community therapy as a public health tool Brazil’s experience centred on Integrative Community Therapy, a methodology developed over 40 years ago by Dr Adalberto Barreto and now used in more than 47 countries. Therapy circles create spaces where participants share experiences and coping strategies, building social support networks at very low cost. Within Brazil’s Unified Health System, the approach has expanded rapidly since its inclusion in national policy in 2017. By 2025, more than five million integrative practice procedures were delivered annually in primary care, with registrations for this therapy increasing by over 500% in eight years. The main challenge now is equity, with access still concentrated in urban areas such as São Paulo. A shared regional direction against a global backdrop Drawing these experiences together, Dr Natalia Aldana of the TCIM Network of the Americas highlighted the shared outcomes of the interventions discussed. Such approaches strengthen healthy behaviours, social networks and community resilience; they are low-cost, adaptable and enable active participation in care, while showing potential to reduce the burden of noncommunicable diseases.The regional momentum mirrors a broader global shift. Dr João Paulo Souz, Director of BIREME (Latin American and Caribbean Center on Health Sciences Information), the specialized center of PAHO/WHO, emphasized that the Global Traditional Medicine Strategy 2025–2034 promotes a vision centred on people, evidence and integrated use. He noted that health should be understood through the relationships between people, community, nature and spirituality. Self-care interventions as a critical pathway towards universal health coverage Dr Manjulaa Narasimhan, Unit Head for Sexual Health and Well-being across the Life-course at WHO, delivered a presentation titled Advancing Self-Care in Traditional Medicine: Aligning the WHO Traditional Medicine Strategy 2025–2034 with the WHO Self-Care Framework, co-authored by Dr Geetha Krishnan Gopalakrishna Pillai, Unit Head for Research, Data and Innovation at the WHO Global Traditional Medicine Centre. WHO recommends self-care interventions for every country and economic setting as a critical path to reach universal health coverage – and traditional medicine acts as a bridge for self-care. Dr Narasimhan explained that traditional medicine contributes most effectively to self-care when embedded directly into primary health care, not operating in parallel, siloed systems. This, she stated, “is possibly the only sustainable way to deliver continuity of care that meets the needs, rights and priorities of communities and individuals”. The Global Traditional Medicine Strategy 2025–2034 is a move towards a more people-centred approach to health care that champions holistic approaches to health equity and patient autonomy.The integration of traditional medicine and self-care requires collaboration across disciplines, clear referral pathways, trained health and care workers, and strong leadership that supports integration. The WHO self-care competency framework defines 10 key competencies for health and care workers to support self-care in their clinical practice, supported by the WHO guideline on self-care interventions for health and well-being. These documents help health-care workers safeguard their own well-being and ensure that care is consistent, safe and grounded in shared standards and values.Together, the discussion through this dialogue points to a fundamental shift in how health systems are designed in the region, and globally, towards models that are more inclusive, culturally-grounded and responsive to the needs of the communities they serve. It is a move towards a system where traditional medicine is respected and responsibly integrated, where self-care provides empowerment and evidence-based care, and where health systems are supportive rather than being distant from these systems.Traditional medicine perspectives from Bolivia, Mexico and Peru - WHO's "Restoring Balance" seriesInnovative Experiences to Strengthen Health and Self-Care through Traditional, Complementary, and Integrative Medicine was streamed in Spanish, with Spanish and Portuguese subtitles. Recordings are available through the CABSIN YouTube channel with Portuguese subtitles and on the BVS MTCI Americas YouTube channel with Spanish subtitles. Dr Narasimhan’s session can be watched in English on both channels.
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Contact: Steven Lee, (210) 450-3823, lees22@uthscsa.edu SAN ANTONIO, Feb. 24, 2026 – Positioning The University of Texas at San Antonio as a national anchor for aging and longevity science, its Sam and Ann Barshop Institute for Longevity and Aging Studies will receive up to $38 million in federal funding for the first nationwide clinical study […]