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Arkansas medical marijuana sales down through June
Medical marijuana sales in Arkansas were $140.3 million in the first six months of 2026, down 2.6% compared with the same period in 2025 when sales totaled $144 million, the...
Arkansas medical marijuana sales down through June
Medical marijuana sales in Arkansas were $140.3 million in the first six months of 2026, down 2.6% compared with the same period in 2025 when sales totaled $144 million, the...
How many cigarettes is wildfire smoke equivalent to? The science explained
Wildfire smoke contains tiny PM2.5 particles that can pose serious health risks. Here's how you can compare prolonged smoke exposure to smoking cigarettes and what it means.
Trump relished in being compared to dictators like Hitler and Stalin, journalist says
The New York Times journalist Jonathan Swan says the president is fixated on becoming a "great man of history" during his second term. Swan's new book, written with Maggie Haberman, is Regime Change.
The Quintessential Trump-era Politician
In 2020, I wrote an article about collaboration and collaborators. The title was History Will Judge the Complicit, and it started with the story of two famous East Germans, Markus Wolf and Wolfgang Leonhard, who both landed in East Berlin just after the end of the war. Although they came from very similar backgrounds, the former stuck with the regime, eventually becoming its spy boss; the latter, horrified by the brutal reality of Soviet-style communism, escaped. By way of juxtaposition, I also wrote about Lyndsey Graham and Mitt Romney. These two men also made very different decisions when confronted by Donald Trump’s assault on the ideals that they had both grown up believing: A glance at their biographies would not have led many to predict what happened next. On paper, Graham would have seemed, in 2016, like the man with deeper ties to the military, to the rule of law, and to an old-fashioned idea of American patriotism and American responsibility in the world. Romney, by contrast, with his shifts between the center and the right, with his multiple careers in business and politics, would have seemed less deeply attached to those same old-fashioned patriotic ideals. Most of us register soldiers as loyal patriots, and management consultants as self-interested. We assume people from small towns in South Carolina are more likely to resist political pressure than people who have lived in many places. Intuitively, we think that loyalty to a particular place implies loyalty to a set of values. But in this case the clichés were wrong. It was Graham who made excuses for Trump’s abuse of power. It was Graham—a JAG Corps lawyer—who downplayed the evidence that the president had attempted to manipulate foreign courts and blackmail a foreign leader into launching a phony investigation into a political rival. It was Graham who abandoned his own stated support for bipartisanship and instead pushed for a hyperpartisan Senate Judiciary Committee investigation into former Vice President Joe Biden’s son. It was Graham who played golf with Trump, who made excuses for him on television, who supported the president even as he slowly destroyed the American alliances—with Europeans, with the Kurds—that Graham had defended all his life. By contrast, it was Romney who, in February, became the only Republican senator to break ranks with his colleagues, voting to impeach the president. “Corrupting an election to keep oneself in office,” he said, is “perhaps the most abusive and destructive violation of one’s oath of office that I can imagine.” In the wake of Graham’s unexpected death on Saturday, I wrote about him again. He was, in many ways, the quintessential politician of Trump’s Washington, a man who shifted 180 degrees. Graham had been a fervent American patriot, proud reserve officer and military lawyer; he became one of the most important defenders of a president who believes soldiers are “suckers and losers” and shows no respect for law of any kind. As his politics changed, his language grew coarser and his behavior grew worse: He urged Israel to “level the place” in Gaza, and insulted the Danish prime minister in Munich, prompting a colleague, Senator Elissa Slotkin, to issue an apology. Here’s an excerpt from the article, which you can read in The Atlantic (gift link here) Like his close friend John McCain, Graham believed that America should stand at the center of a broad democratic alliance. The few times I met him were in that context, at the Munich Security Conference or at other European-American gatherings, which he once attended with great regularity. He also took seriously the practice of American democracy at home. In a 2014 conversation with The Atlantic, he described himself as a pragmatic politician who eschewed populist slogans. “I know Washington is broken, but what’s broken about it is everybody yelling and nobody trying to fix it,” he said. “I’m trying.” When Donald Trump first appeared in U.S. politics, Graham recognized him immediately for what he was: the spokesman for an alien ideology, one radically different from the idealistic patriotism that Graham had practiced and preached since childhood. Trump privately mocked the military that Graham loved as nothing but “suckers and losers.” He put falsehoods, cynicism, and personal greed at the center of his politics, while expressing disdain for transparency, accountability, and democracy itself. In 2015, Graham described Trump as a “race-baiting, xenophobic, religious bigot” who should “go to hell.” Also as a “nutjob.” When Trump won, Graham understood, as did so many others, that he would have to make some important choices. For a while, he went silent. In the spring of 2016, I saw him at one of those conferences in Europe. He seemed too depressed to speak. But then, like many other Republicans—and, more important, like many other people who have lived under political occupation or experienced radical regime change—he made the decision to abandon his previous ideals, to bury the patriotism that was once so important to him, and to become, instead, a loud, opportunistic collaborator. As I wrote in 2020, it’s very hard to look in to anyone’s heart and to understand their motivations from the outside. But it does seem that Graham had a deep need to be relevant, to be in the game, to be a player, at any cost. And that, rather than any particular achievement, is what he will be remembered for. Continuing to monitor conflicts of interest, ostentatious emoluments, outright corruption and policy changes that will facilitate outright corruption. (Read my original article, Kleptocracy Inc and check out the SNF Agora Institute chart) June 27 Trump diverted funds from more than 900 projects nationwide—including safety upgrades at national parks—to finance beautification projects around Washington, including $700,000 used to fix a White House walkway. June 28 After a years-long Department of Justice investigation into Abbott Laboratories over its management of a baby formula facility where potentially deadly bacteria turned up enough evidence to criminally charge the firm, top department officials instead opted for a financial penalty—illustrating the department’s move away from strict corporate enforcement under the Trump administration. June 29 Defense Secretary Pete Hegseth stacked the Defense Policy Board—which is charged with providing independent national security recommendations to the Office of the Secretary of Defense—with Trump allies, including tech billionaire Marc Andreessen, who has invested in defense contractors like OpenAI and SpaceX, after purging the panel last year. Trump purchased between $1 million and $5 million worth of stock in Axon—which makes about 90% of US tasers—two weeks before ICE solicited bids for a $220 million taser contract. The White House unveiled limited-edition US passports for America’s 250th birthday, featuring a portrait of Trump overlaid on the Declaration of Independence. June 30 Trump made around $1.2 billion from his various crypto companies last year, including more than $500 million from World Liberty Financial and $600 million from sales of his meme coin. Trump is considering issuing 250 pardons to commemorate America’s 250th birthday. These are expected to be largely doled out through the Office of the Pardon Attorney’s informal network of intermediaries. White-collar defense attorneys say they can typically win pardons for clients for $2 million. Last year, Trump administration officials awarded a no-bid contract worth up to $500 million to Clark Construction for the White House ballroom—negotiations in which the president was directly involved. July 1 The Amazon MGM Studios documentary about Melania Trump earned the First Lady more than $10 million. Trump made hundreds of individual stock purchases worth as much as $12.8 million the day before his announcement pausing “Liberation Day” tariffs for 90 days sent the S&P 500 soaring nearly 10%—one of the largest single-day gains ever. Mar-a-Lago and Trump National Doral saw record surges in revenue during Trump’s first year in office, earning $77.5 million and $121.9 million respectively, compared to average annual revenues of $25 million and $75 million during Trump’s first term. Entities across the Persian Gulf paid more than $300 million to Trump’s businesses last year, including $263 million from the sale of half his stake in World Liberty Financial to a firm backed by Sheikh Tahnoon bin Zayed Al Nahyan, a top UAE royal and brother of the country’s president. Kash Patel failed to properly disclose his stock purchase of a Department of Justice contractor, MicroStrategy, worth up to $250,000 last year, despite laws requiring disclosure within 45 days of the trade. July 2 Trump has been directing administration officials to steer more government contracts—so far worth more than $2.4 billion—to Jeff Bezos’s Blue Origin aerospace firm, after Amazon gave $1 million to Trump’s inaugural fund, donated to the White House ballroom project, and reached a $40 million deal to license a Melania Trump documentary. Alongside the billions he made from cryptocurrency last year, Trump earned millions of dollars from real estate licensing fees, including $21 million from organizations in the UAE and $9.2 million in Saudi Arabia, along with smaller sums from merchandise sales, including $4.7 million from Trump Watches and $35,920 from 45 Guitar, a guitar brand endorsed by the president. House Democrats accused consultants tied to the Trump administration of financial fraud, alleging they gave prospective donors to America250—the bipartisan committee created by Congress to celebrate the nation’s semiquincentennial—the banking and routing numbers for Freedom 250, the White House’s fund for events like the UFC cage fight. Democrats also allege that Freedom 250’s CEO traveled to the World Economic Forum in Davos to solicit donations from foreign government officials and business leaders. The Trump administration’s proposed rollback of gun regulations would lift the prohibition on mailing handguns to individuals, a significant win for GrabAGun—the direct-to-consumer firearm retailer on whose board Donald Trump Jr. sits and in which he holds a 1.1% ownership stake. GrabAGun currently must rely on a middleman to transfer firearms to customers. Trump’s financial disclosure revealed that he made more than 21,000 securities trades during his first year back in office, growing his investment account to at least $858 million across stakes in around 1,600 companies, compared to the 13 total trades Joe Biden made during his entire presidency. July 4 Nearly 1 million people who bought the president’s meme coin—Trump Coin—collectively lost $3.8 billion on the token, while he walked away with $636 million. July 6 Shares of Dell closed up 4.4% after Trump recommended people buy the firm’s computers during a speech announcing the launch of Trump investment accounts for children, boosting the more than $1 million worth of shares the president owns in the company. July 9 Federal Reserve Chair Kevin Warsh named crypto investor and Trump ally Marc Andreessen to a task force aimed at modernizing the Federal Reserve, including rethinking its approach to inflation—a key factor keeping interest rates elevated. Palm Beach International Airport has officially changed its name to Trump International Airport—a move that will require Florida to pay royalties to the president’s family for licensing his name. I was there a few months ago, during a trip to Barcelona. The sculptures were a revelation, I’d never seen so many together before. Each one has its own personality. Enjoy the good weather, if you are lucky enough to have it. History Will Judge the Complicit The Quintessential Politician Autocracy Inc Open Letters, from Anne Applebaum is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Subscribe
World Cup 2026: France, Spain, England & Argentina statistical analysis
BBC Sport compares statistics for France, Spain, Argentina and England in the World Cup so far as the semi-finalists prepare to meet.
How Titans' Tony Pollard compares to other starting running backs?
Tony Pollard lands in the middle of PFF rankings as the Tennessee Titans prepare for the upcoming NFL season.
Assassin's Creed Black Flag Resynced opens to 2 million sales on launch day
Ubisoft tends not to share sales figures publicly – which means we can't even compare it to the sales of prior Assassin's Creed games, let alone its peers.
Political expert says Platner’s exit unprecedented in modern Maine politics
University of Southern Maine political science professor Ron Schmidt says he has never seen a U.S. Senate candidate in Maine withdraw this close to Election Day. He compared the Graham Platner situation to the 2024 presidential race, when…
Missouri homebuilders report dwindling business amid national housing slump
The housing industry saw a sharp drop in construction starts nationwide in May, both compared with the previous month and with the same period a year earlier. The broader Midwest region showed resilience, but Missouri builders still reported weaker business activity during this time. According to the U.S. Census Bureau, privately owned housing starts across […]
MAGA's attack on science is even worse than it looks
In my post on America’s 250-year anniversary, I argued that respect for the individual was the “secret sauce” in the U.S.’ long-lasting preeminence among nations. But it was certainly not the only factor. There were plenty of institutional innovations that helped the U.S. stay on top — economically, militarily, and in terms of the attractiveness of its society. One of these was American science. Today we take things like the modern research university, government grants for science, public-to-private research spinoffs, etc. for granted, but a lot of that infrastructure wasn’t there before World War 2. It was either invented or scaled up massively by the U.S. government in the postwar period, led by far-sighted scientist-bureaucrats like Vannevar Bush (pictured above). If you want to read about this history, a good place to start would be Jonathan Gruber and Simon Johnson’s excellent book Jump-Starting America. Those scientific institutions didn’t exist in a vacuum, however. They were backed by the U.S. government’s abiding faith in the power of science — and, even more fundamentally, by deep popular trust in the scientific enterprise. Science gave us radar and the atom bomb in wartime, and in peacetime it gave us plastics, vaccines, cheap food, and a thousand other things that made our lives easier. Science was also the driver behind American industry — chemicals, aerospace, telecommunications, computers, electronics, and so on. We owed science our jobs, our livelihoods, our comfortable living standards, and our greatness and power as a nation. It’s little wonder that both political parties, even as they fought viciously over other issues, were steadfast in their support of science. For a long time, the only people who distrusted science were a hippie fringe on the left who disliked modernity (or thought they did, anyway), who also disliked American industry and American power, and who subscribed to an early version of degrowth environmentalism. America’s scientific enterprise is still strong, especially compared to the systems in Europe, Japan, Korea, and other developed countries. It has lost a lot of ground to China in a relative sense, but a lot of that is because of China’s incredible growth; as China has poured untold amounts of money and talent into its research labs, spending and output have overtaken the U.S. by some measures. There are a few ways in which China’s rise creates problems for American science — for example, top scientists can choose to work in China instead of in the U.S. — and of course there’s the concern that China’s technological strength will help its military to reign supreme. But overall, China’s rise in science should be good for American science, since American scientists can use Chinese discoveries for free and build on them. The much bigger problem is that the scientific enterprise America built during and after World War 2 is now being threatened with absolute decline. The biggest problem, of course, is that much of the country — the Republican half — has basically lost faith in the scientific enterprise. To what degree this loss of faith is justified is an open question, and deserves to be discussed openly. But the larger point — that the system that powered American dominance is under threat — is true either way. Most Americans still trust science, but MAGA Republicans don’t There’s a myth, popular in right-wing circles, that scientists have lost the trust of regular Americans — either due to the increasingly left-wing composition of academic departments, or to misbehavior during Covid, or to DEI-related research taking over science, etc. This also fits with a wider narrative that Americans are losing trust in all of our institutions. But it just isn’t true. Poll after poll shows that on the whole, Americans still trust scientists and want to spend more on science. For example, here’s a Pew poll from late 2025 showing that although about a fifth of Republicans did lose confidence in science in 2020, two thirds still have at least “a fair amount” of confidence: That same poll found that scientists are among America’s most trusted groups — even better trusted than the military! In fact, Americans trust scientists more than people in most other countries do. Other polls show that although Republican trust in science has dropped somewhat, Republican support for spending more on science remains very strong: A Pew poll in 2023 found the exact same thing: So when MAGA types tell you about scientists losing their credibility, or a drop in trust in science, they’re only talking about themselves. Whatever left-wing politicization of science happened during the Biden administration — and there was definitely some of that — it was not enough to make most Republicans lose faith in the scientific enterprise, or favor research cuts in order to purge unwanted ideology from the system. That doesn’t mean I think progressives should continue down the path of politicizing scientific research. They should not, and Biden made real missteps in this area. Objectivity clearly matters for public trust of science in the long term. But as of right now, there’s no crisis of trust in science, except among the smallish minority of people who are running around screaming that there’s a crisis of trust in science. But despite science’s overwhelming popularity and public trust, Trump and his administration are launching an unprecedented and devastating attack on American science — cutting funding, and forcing science projects to undergo ideological review by government commissars. Trump’s comprehensive attack on science
iPhone 18 Pro Max Component Costs Could Rise Nearly $300
The bill of materials for the iPhone 18 Pro Max is expected to rise by nearly $300 compared to the iPhone 17 Pro Max, according to a new Counterpoint Research analysis. The estimate covers the 1TB storage model. NAND flash costs for the device are said to exceed $250 on their own, a figure that would cover roughly half of the iPhone 17 Pro Max's entire estimated component cost.
Pokémon TCG: Pitch Black Sealed Booster Bundles Preorders Are Finally Back in Stock at Amazon, and They're Way Under Market Price Compared TCGplayer
Pokémon TCG: Pitch Black Sealed Booster Bundles preorders are back in stock at Amazon, and while it's not at MSRP, they're still listed for way under market price compared to resale sites like TCGplayer and eBay.
Just Don’t Use the Super-Long Zoom on Sony’s RX10 V for Anything Creepy, Okay?
Your iPhone can't compare to this camera's built-in zoom.
Black Ops 1 & 2 Now Live On PS5, Here's Some Early Footage Compared To Xbox
A surprise shadow drop
Trying store-bought mayonnaise to find best one: ranking, taste test
I compared Miracle Whip and different Kraft, Kewpie, Duke's, Hellmann's mayonnaises, tasting them on their own and in chicken salad to find the best.
Where does the Tennessee Titans offensive line rank?
The Tennessee Titans made a handful of changes to its offensive line during the offseason, so how do they rank compared to the rest of the NFL?
The ‘Assassin’s Creed Black Flag Resynced’ Metacritic Review Score Is In
Assassin's Creed Black Flag Resynced reviews are in, and the game now has a Metacritic score to compare to the original.
Shohei Ohtani hits 300th career home run
While Tuesday’s contest against the Colorado Rockies produced a more disappointing outcome compared to Monday’s walk-off, Shohei Ohtani started off the game with a historic blast. Ohtani smacked a leadoff home run against Michael Lorenzen on Tuesday, which was not only his 25th leadoff home run as a Dodger, but the 300th home run of […]
Scientists used AI to crack one of water's biggest mysteries
Water’s odd behavior becomes even more dramatic when it is supercooled, but scientists have struggled to compare the many different ways of describing its microscopic structure. Researchers at the University of Osaka used an AI model trained on computer simulations to evaluate 16 different structural descriptors. The system identified the most effective ways to distinguish between water’s two competing liquid states, providing a clearer framework for studying one of nature’s most mysterious substances.
Analyzing Argentina's Historic Comeback vs. Egypt From A Betting Perspective
How does Argentina's improbable comeback compare to other miraculous comebacks in sports from a live betting perspective?
Foldable iPhone Ultra vs. iPad: How their screens reportedly compare
When opened, the foldable iPhone may scratch users' iPad itch too.
Google announces August 12 event for Pixel 11 and Pixel Watch 5
Made by Google 2026 is officially taking place on Wednesday, August 12. This is a week earlier compared to last year...
Are Consumer Discretionary Stocks Lagging AMC Entertainment (AMC) This Year?
Here is how AMC Entertainment (AMC) and Bassett Furniture (BSET) have performed compared to their sector so far this year.
Here's How the iPhone 18 Pro Max Will Compare to the iPhone 18 Pro
Apple is expected to unveil the iPhone 18 Pro and iPhone 18 Pro Max in September. The devices will have a lot in common, but the latest leak suggests that the Pro Max model will take an even bigger step forward in regards to battery life. According to new filings in a Chinese regulatory database, it appears that the iPhone 18 Pro Max will be equipped with a 5,567mAh battery in the U.S., while the iPhone 18 Pro will seemingly have a 4,288mAh battery.
Deutsche Bank, Lufthansa invest in SAF to cut business travel emissions
The deal between the German lender and the aviation company is estimated to enable up to 5,500 metric tons of carbon dioxide emissions reductions compared to conventional jet fuel.
State Street Health Care ETF Tops Invesco Pharma on Cost and Size
Compare sector breadth, yield, and recent performance as these two healthcare ETFs take different approaches to growth and income.
Vanguard Health Care vs. iShares Biotech: How Do These ETFs Stack Up?
Broad healthcare diversification or concentrated biotech growth? Compare cost, risk, and portfolio composition to find your fit.
U.S. death rate fell to lowest level on record in 2025, CDC says
The rate of deaths fell for all age groups compared with 2024, according to preliminary data.
Ford sales struggle with no Escape, F-Series woes, less rental business
Ford struggled compared to crosstown competitors General Motors Co. and Stellantis NV.
Team's prediction task compares GPT-4o with classic machine learning
Large language models have been functionally opaque. Seeking transparency, a team undertook a comparison with traditional machine learning.
I compared same breakfast sandwich from Starbucks, Dunkin', Tim Hortons
I compared sausage, egg, and cheese breakfast sandwiches from Starbucks, Dunkin', and Tim Hortons. My favorite had a flavorful egg and creamy cheese.
What Makes Live Nation Entertainment (LYV) a Good Investment Bet?
TimesSquare Capital Management, an equity investment management company, released its “U.S. Mid Cap Growth Strategy” first-quarter 2026 investor letter. A copy of the letter can be downloaded here. The Strategy fell 7.72% (net) in the quarter compared to -6.35% for the Russell Midcap Growth Index. In the first quarter, markets navigated geopolitical tensions and economic resilience alongside […]
SPDR Health Care ETF Tops iShares Biotech in Long-Term Returns
Compare portfolio diversity, risk profiles, and income potential as you weigh these two leading healthcare ETFs.
Trump's new Air Force One: differences, design, cost, photos of inside
Donald Trump flew on the new Air Force One, a luxury jet gifted from Qatar, for the first time. Here's how it compares to the old presidential plane.
Is Accel Entertainment (ACEL) Outperforming Other Consumer Discretionary Stocks This Year?
Here is how Accel Entertainment (ACEL) and American Outdoor Brands, Inc. (AOUT) have performed compared to their sector so far this year.
Simpler lung cancer screening criteria could capture more patients likely to benefit
New research published in JAMA Internal Medicine compared current USPSTF criteria to a potential shift toward total years smoked, rather than pack years.
Trump made more than $1bn from crypto in first year back in office
It compares with more than $600m in total income that Trump reported in his last financial disclosure.
Politics: High Cost Of Healthcare And The Political Fallout
Americans are grappling with a rising cost in healthcare. For the first time in five years, fewer than half of Americans can consistently afford healthcare. That’s according to the latest data from Gallup. The cost of healthcare in the US remains higher than any other large, wealthy country. According to a report from the Department of Health and Human Services, five million fewer people are enrolled in marketplace plans for 2026, compared to last year. That’s as average premium costs rose about 58%.With health care costs increasingly becoming a stress for Americans, voters are now making the issue a top priority at the ballot box. And it’s an issue that crosses party and geographic lines. According to new polling from The Century Foundation, 71% of Democrats, 66% of Republicans and 75% of rural voters agree that reigning in hospital costs should be a top priority for lawmakers. While new polling from Ipsos and Axios suggests that a majority of Americans say they’re more likely to vote for candidates in November who will lower their health costs.How will the issue of healthcare shape the midterms? And how are Americans grappling with the cost of their health?Find more of our programs online. Listen to 1A sponsor-free by signing up for 1A+ at plus.npr.org/the1a.
Politics: High Cost Of Healthcare And The Political Fallout
Americans are grappling with a rising cost in healthcare. For the first time in five years, fewer than half of Americans can consistently afford healthcare. That’s according to the latest data from Gallup. The cost of healthcare in the US remains higher than any other large, wealthy country. According to a report from the Department of Health and Human Services, five million fewer people are enrolled in marketplace plans for 2026, compared to last year. That’s as average premium costs rose about 58%.With health care costs increasingly becoming a stress for Americans, voters are now making the issue a top priority at the ballot box. And it’s an issue that crosses party and geographic lines. According to new polling from The Century Foundation, 71% of Democrats, 66% of Republicans and 75% of rural voters agree that reigning in hospital costs should be a top priority for lawmakers. While new polling from Ipsos and Axios suggests that a majority of Americans say they’re more likely to vote for candidates in November who will lower their health costs.How will the issue of healthcare shape the midterms? And how are Americans grappling with the cost of their health?Find more of our programs online. Listen to 1A sponsor-free by signing up for 1A+ at plus.npr.org/the1a.
Galaxy Z Fold 8 comparison shot shows massive height difference between phones
At this point, it’s clear that Samsung is planning to launch a Galaxy Z Fold 8 Ultra and Z Fold...
France Recorded 1,000 Excess Deaths During Heat Wave, Officials Say
First estimates published by the national health agency listed hundreds more deaths per day compared with the daily death rate in previous months.
Jaguars’ New DT Compared to All-Pro Chris Jones
Jacksonville’s interior defense gains a high-upside disruptor in Ruke Orhorhoro, a raw Clemson product whose elite frame and athleticism draw striking comparisons to the Chiefs’ superstar anchor.
Polish scientists compare five kombuchas, and the tea in each changes far more than flavor
"The most surprising aspect was the scale of changes occurring during fermentation."
Efzimfotase alfa demonstrated improvements in bone health in treatment-naïve pediatric patients with hypophosphatasia achieving median difference of 1.67 vs placebo in RGI-C Score at week 25 in MULBERRY Phase III trial
Positive results from the MULBERRY Phase III trial showed that efzimfotase alfa (ALXN1850), an investigational enzyme replacement therapy, demonstrated a statistically significant and clinically meaningful improvement in bone health in children (2 to
🥜 Still nutty after 50 years
Center City snack shop Nuts To You marks 50 years in business and looks to TikTok for sustained growth. Plus: Olney update here TK, and we compare Revolutionary history in Philly vs Boston.
Spent 6 hours in Amtrak coach and business class, how they compare
I splurged for business class on an Amtrak ride from New York to Vermont. Here's what my $80 upgrade got me.
Millions drop Affordable Care Act coverage after subsidies expired
New federal figures show that about 3 million fewer people had Affordable Care Act health insurance plans in February compared with the same time last year.
The Simplest Focus Booster Is Sitting in Your Drawer Right Now
Science shows that writing by hand strengthens the brain by improving memory, focus, and deep thinking compared to typing.
Millions drop Obamacare health coverage after subsidies expire and costs rise
NEW YORK — About 3 million fewer people in the United States had Affordable Care Act health insurance plans in February compared with the same time...
Millions drop Obamacare health coverage after subsidies expire and costs rise
About 3 million fewer people in the United States had Affordable Care Act health insurance plans in February compared with the same time last year, according to new federal data. In the report releas...
Millions drop Obamacare health plans after subsidies expire and costs rise
New federal figures show that about 3 million fewer people had Affordable Care Act health insurance plans in February compared with the same time last year.
Millions drop Obamacare health coverage after subsidies expire and costs rise
New federal figures show that about 3 million fewer people had Affordable Care Act health insurance plans in February compared with the same time last year. The Department of Health and Human Services...
Millions drop Obamacare coverage after subsidies expire, costs rise
New federal figures show that about 3 million fewer people had Affordable Care Act health insurance plans in February compared with the same time last year
Vance Downplays Watergate and Compares Himself to Nixon
The vice president said that the scandal that ended Richard Nixon’s presidency would be “like a 12-hour news story” if it happened today and that the “deep state” had taken down Nixon.
Corus Entertainment reports $36.5M Q3 loss, revenue down 26 per cent from year ago
TORONTO — Corus Entertainment Inc. reported a net loss attributable to shareholders of $36.5 million in its latest quarter as the company behind Global Television saw its revenue fall 16 per cent compared with a year ago.
Here's How The New Xbox Prices Compare To PS5, Switch 2 & Steam Machine
Going live as of August 2026
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.
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WHO Step-by-Step programme recognized as a promising innovation for mental health in humanitarian settings
WHO, in collaboration with the Pan American Health Organization (PAHO) and Lebanon’s National Mental Health Programme, showcased Step-by-Step at the World Bank Group’s biennial Fragility Forum in Washington, DC. The forum convened more than 1500 representatives from governments, international organizations and civil society to advance the development agenda in the world’s most fragile and crisis-affected communities. WHO was invited to demonstrate Step-by-Step – a digital self-help intervention for depression, alongside innovations presented by Stanford University and Google. The World Bank Group selected Step-by-Step from among more than 50 innovations that they reviewed in the lead up to the Forum.About Step-by-StepStep-by-Step is a free digital tool developed by WHO in collaboration with Lebanon’s National Mental Health Programme and other partners, that helps people manage depression and anxiety on their own, with light guidance from a trained supporter. It uses a picture-based story to walk users through evidence-based techniques for managing common mental health conditions. Users complete five modules, with weekly contacts – of around 15 minutes each – from a trained non-specialist helper, such as a community health worker.Evidence-basedClinical trials in Lebanon involving nearly 1250 participants – including 680 Lebanese participants and 569 Syrian participants – showed that Step-by-Step significantly reduced depression, anxiety and stress, and improved daily functioning. By the end of the programme, a reduction in depressive symptoms of more than 50% was reported by almost half (46%) of Lebanese participants and more than one third (37%) of Syrian participants compared to approximately 14% of those who did not use the programme. Cost-effectiveness analyses further suggest that Step-by-Step may offer a cost-saving response to depression in humanitarian emergencies in low- and middle-income countries.Used globallyStep-by-Step is already in use nationwide in Lebanon and Thailand, as part of each country’s mental health system. In India, the NGO – Kaya Guides – has adapted Step-by-Step for delivery through a widely used messaging app. Together, these programmes have provided thousands of people with evidence-based support for depression. The content is freely available from the WHO website for any organization or country to adapt for their own context.A new implementation guideThe Fragility Forum coincided with the recent launch of WHO’s new practical guide: Psychological self-help interventions: delivering self-help for individuals, featuring Step-by-Step and Doing What Matters in Times of Stress (WHO, 2026). The guide is designed to help governments, health workers, organizations and planners bring evidence-based mental health support to more people, particularly in low-resource and crisis-affected settings.
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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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Stellar Blade CEO Brushes Off Complaints About Sequel's Protagonist
CEO Hyung-tae Kim stated that while Evie is 'younger' and 'smaller in size' compared to Stellar Blade's protagonist, the outfits in the sequel will be 'even more appealing' for fans
POP Culture Group Reports Stellar Half-Year Results: Robust Business Growth with Revenue Surging 141% Year-Over-Year
During the reporting period, the Company recorded net revenue of $68,902,790, representing a year-over-year increase of 65% compared to $41,813,266 in the same period of 2024, marking a leapfrog expansion in business scale. Revenue from the digital entertainment segment increased from $37.18 million for the six months ended December 31, 2024 to $66.57...
Brewers can’t keep up with Athletics, lose 7-5
The Athletics kept mashing home runs, hitting five more compared to the Brewers’ one
Reflecting On Media & Entertainment Stocks’ Q4 Earnings: EchoStar (NASDAQ:SATS)
Quarterly earnings results are a good time to check in on a company’s progress, especially compared to its peers in the same sector. Today we are looking at EchoStar (NASDAQ:SATS) and the best and worst performers in the media & entertainment industry.