Tech
The great chip crisis threatens the promise of Moore’s Law
Published
2 years agoon
By
Drew Simpson
Even as microchips have become essential in so many products, their development and manufacturing have come to be dominated by a small number of producers with limited capacity—and appetite—for churning out the commodity chips that are a staple for today’s technologies. And because making chips requires hundreds of manufacturing steps and months of production time, the semiconductor industry cannot quickly pivot to satisfy the pandemic-fueled surge in demand.
After decades of fretting about how we will carve out features as small as a few nanometers on silicon wafers, the spirit of Moore’s Law—the expectation that cheap, powerful chips will be readily available—is now being threatened by something far more mundane: inflexible supply chains.
A lonely frontier
Twenty years ago, the world had 25 manufacturers making leading-edge chips. Today, only Taiwan Semiconductor Manufacturing Company (TSMC) in Taiwan, Intel in the United States, and Samsung in South Korea have the facilities, or fabs, that produce the most advanced chips. And Intel, long a technology leader, is struggling to keep up, having repeatedly missed deadlines for producing its latest generations.
One reason for the consolidation is that building a facility to make the most advanced chips costs between $5 billion and $20 billion. These fabs make chips with features as small as a few nanometers; in industry jargon they’re called 5-nanometer and 7-nanometer nodes. Much of the cost of new fabs goes toward buying the latest equipment, such as a tool called an extreme ultraviolet lithography (EUV) machine that costs more than $100 million. Made solely by ASML in the Netherlands, EUV machines are used to etch detailed circuit patterns with nanometer-size features.
Chipmakers have been working on EUV technology for more than two decades. After billions of dollars of investment, EUV machines were first used in commercial chip production in 2018. “That tool is 20 years late, 10x over budget, because it’s amazing,” says David Kanter, executive director of an open engineering consortium focused on machine learning. “It’s almost magical that it even works. It’s totally like science fiction.”
Such gargantuan effort made it possible to create the billions of tiny transistors in Apple’s M1 chip, which was made by TSMC; it’s among the first generation of leading-edge chips to rely fully on EUV.
Only the largest tech companies are willing to pay hundreds of millions of dollars to design a chip for leading-edge nodes.
Paying for the best chips makes sense for Apple because these chips go into the latest MacBook and iPhone models, which sell by the millions at luxury-brand prices. “The only company that is actually using EUV in high volume is Apple, and they sell $1,000 smartphones for which they have insane margin,” Kanter says.
Not only are the fabs for manufacturing such chips expensive, but the cost of designing the immensely complex circuits is now beyond the reach of many companies. In addition to Apple, only the largest tech companies that require the highest computing performance, such as Qualcomm, AMD, and Nvidia, are willing to pay hundreds of millions of dollars to design a chip for leading–edge nodes, says Sri Samavedam, senior vice president of CMOS technologies at Imec, an international research institute based in Leuven, Belgium.
Many more companies are producing laptops, TVs, and cars that use chips made with older technologies, and a spike in demand for these is at the heart of the current chip shortage. Simply put, a majority of chip customers can’t afford—or don’t want to pay for—the latest chips; a typical car today uses dozens of microchips, while an electric vehicle uses many more. It quickly adds up. Instead, makers of things like cars have stuck with chips made using older technologies.
What’s more, many of today’s most popular electronics simply don’t require leading-edge chips. “It doesn’t make sense to put, for example, an A14 [iPhone and iPad] chip in every single computer that we have in the world,” says Hassan Khan, a former doctoral researcher at Carnegie Mellon University who studied the public policy implications of the end of Moore’s Law and currently works at Apple. “You don’t need it in your smart thermometer at home, and you don’t need 15 of them in your car, because it’s very power hungry and it’s very expensive.”
The problem is that even as more users rely on older and cheaper chip technologies, the giants of the semiconductor industry have focused on building new leading-edge fabs. TSMC, Samsung, and Intel have all recently announced billions of dollars in investments for the latest manufacturing facilities. Yes, they’re expensive, but that’s where the profits are—and for the last 50 years, it has been where the future is.
TSMC, the world’s largest contract manufacturer for chips, earned almost 60% of its 2020 revenue from making leading-edge chips with features 16 nanometers and smaller, including Apple’s M1 chip made with the 5-nanometer manufacturing process.
Making the problem worse is that “nobody is building semiconductor manufacturing equipment to support older technologies,” says Dale Ford, chief analyst at the Electronic Components Industry Association, a trade association based in Alpharetta, Georgia. “And so we’re kind of stuck between a rock and a hard spot here.”
Low-end chips
All this matters to users of technology not only because of the supply disruption it’s causing today, but also because it threatens the development of many potential innovations. In addition to being harder to come by, cheaper commodity chips are also becoming relatively more expensive, since each chip generation has required more costly equipment and facilities than the generations before.
Some consumer products will simply demand more powerful chips. The buildout of faster 5G mobile networks and the rise of computing applications reliant on 5G speeds could compel investment in specialized chips designed for networking equipment that talks to dozens or hundreds of Internet-connected devices. Automotive features such as advanced driver-assistance systems and in-vehicle “infotainment” systems may also benefit from leading-edge chips, as evidenced by electric-vehicle maker Tesla’s reported partnerships with both TSMC and Samsung on chip development for future self-driving cars.
But buying the latest leading-edge chips or investing in specialized chip designs may not be practical for many companies when developing products for an “intelligence everywhere” future. Makers of consumer devices such as a Wi-Fi-enabled sous vide machine are unlikely to spend the money to develop specialized chips on their own for the sake of adding even fancier features, Kanter says. Instead, they will likely fall back on whatever chips made using older technologies can provide.
The majority of today’s chip customers make do with the cheaper commodity chips that represent a trade-off between cost and performance.
And lower-cost items such as clothing, he says, have “razor-thin margins” that leave little wiggle room for more expensive chips that would add a dollar—let alone $10 or $20—to each item’s price tag. That means the climbing price of computing power may prevent the development of clothing that could, for example, detect and respond to voice commands or changes in the weather.
The world can probably live without fancier sous vide machines, but the lack of ever cheaper and more powerful chips would come with a real cost: the end of an era of inventions fueled by Moore’s Law and its decades-old promise that increasingly affordable computation power will be available for the next innovation.
The majority of today’s chip customers make do with the cheaper commodity chips that represent a trade-off between cost and performance. And it’s the supply of such commodity chips that appears far from adequate as the global demand for computing power grows.
“It is still the case that semiconductor usage in vehicles is going up, semiconductor usage in your toaster oven and for all kinds of things is going up,” says Willy Shih, a professor of management practice at Harvard Business School. “So then the question is, where is the shortage going to hit next?”
A global concern
In early 2021, President Joe Biden signed an executive order mandating supply chain reviews for chips and threw his support behind a bipartisan push in Congress to approve at least $50 billion for semiconductor manufacturing and research. Biden also held two White House summits with leaders from the semiconductor and auto industries, including an April 12 meeting during which he prominently displayed a silicon wafer.
The actions won’t solve the imbalance between chip demand and supply anytime soon. But at the very least, experts say, today’s crisis represents an opportunity for the US government to try to finally fix the supply chain and reverse the overall slowdown in semiconductor innovation—and perhaps shore up the US’s capacity to make the badly needed chips.
An estimated 75% of all chip manufacturing capacity was based in East Asia as of 2019, with the US share sitting at approximately 13%. Taiwan’s TSMC alone has nearly 55% of the foundry market that handles consumer chip manufacturing orders.
Looming over everything is the US-China rivalry. China’s national champion firm SMIC has been building fabs that are still five or six years behind the cutting edge in chip technologies. But it’s possible that Chinese foundries could help meet the global demand for chips built on older nodes in the coming years. “Given the state subsidies they receive, it’s possible Chinese foundries will be the lowest-cost manufacturers as they stand up fabs at the 22-nanometer and 14-nanometer nodes,” Khan says. “Chinese fabs may not be competitive at the frontier, but they could supply a growing portion of demand.”
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Tech
Newly revealed coronavirus data has reignited a debate over the virus’s origins
Published
46 mins agoon
03/24/2023By
Drew Simpson
Data collected in 2020—and kept from public view since then—potentially adds weight to the animal theory. It highlights a potential suspect: the raccoon dog. But exactly how much weight it adds depends on who you ask. New analyses of the data have only reignited the debate, and stirred up some serious drama.
The current ruckus starts with a study shared by Chinese scientists back in February 2022. In a preprint (a scientific paper that has not yet been peer-reviewed or published in a journal), George Gao of the Chinese Center for Disease Control and Prevention (CCDC) and his colleagues described how they collected and analyzed 1,380 samples from the Huanan Seafood Market.
These samples were collected between January and March 2020, just after the market was closed. At the time, the team wrote that they only found coronavirus in samples alongside genetic material from people.
There were a lot of animals on sale at this market, which sold more than just seafood. The Gao paper features a long list, including chickens, ducks, geese, pheasants, doves, deer, badgers, rabbits, bamboo rats, porcupines, hedgehogs, crocodiles, snakes, and salamanders. And that list is not exhaustive—there are reports of other animals being traded there, including raccoon dogs. We’ll come back to them later.
But Gao and his colleagues reported that they didn’t find the coronavirus in any of the 18 species of animal they looked at. They suggested that it was humans who most likely brought the virus to the market, which ended up being the first known epicenter of the outbreak.
Fast-forward to March 2023. On March 4, Florence Débarre, an evolutionary biologist at Sorbonne University in Paris, spotted some data that had been uploaded to GISAID, a website that allows researchers to share genetic data to help them study and track viruses that cause infectious diseases. The data appeared to have been uploaded in June 2022. It seemed to have been collected by Gao and his colleagues for their February 2022 study, although it had not been included in the actual paper.
Tech
Fostering innovation through a culture of curiosity
Published
6 hours agoon
03/24/2023By
Drew Simpson
And so I think a big part of it as a company, by setting these ambitious goals, it forces us to say if we want to be number one, if we want to be top tier in these areas, if we want to continue to generate results, how do we get there using technology? And so that really forces us to throw away our assumptions because you can’t follow somebody, if you want to be number one you can’t follow someone to become number one. And so we understand that the path to get there, it’s through, of course, technology and the software and the enablement and the investment, but it really is by becoming goal-oriented. And if we look at these examples of how do we create the infrastructure on the technology side to support these ambitious goals, we ourselves have to be ambitious in turn because if we bring a solution that’s also a me too, that’s a copycat, that doesn’t have differentiation, that’s not going to propel us, for example, to be a top 10 supply chain. It just doesn’t pass muster.
So I think at the top level, it starts with the business ambition. And then from there we can organize ourselves at the intersection of the business ambition and the technology trends to have those very rich discussions and being the glue of how do we put together so many moving pieces because we’re constantly scanning the technology landscape for new advancing and emerging technologies that can come in and be a part of achieving that mission. And so that’s how we set it up on the process side. As an example, I think one of the things, and it’s also innovation, but it doesn’t get talked about as much, but for the community out there, I think it’s going to be very relevant is, how do we stay on top of the data sovereignty questions and data localization? There’s a lot of work that needs to go into rethinking what your cloud, private, public, edge, on-premise look like going forward so that we can remain cutting edge and competitive in each of our markets while meeting the increasing guidance that we’re getting from countries and regulatory agencies about data localization and data sovereignty.
And so in our case, as a global company that’s listed in Hong Kong and we operate all around the world, we’ve had to really think deeply about the architecture of our solutions and apply innovation in how we can architect for a longer term growth, but in a world that’s increasingly uncertain. So I think there’s a lot of drivers in some sense, which is our corporate aspirations, our operating environment, which has continued to have a lot of uncertainty, and that really forces us to take a very sharp lens on what cutting edge looks like. And it’s not always the bright and shiny technology. Cutting edge could mean going to the executive committee and saying, Hey, we’re going to face a challenge about compliance. Here’s the innovation we’re bringing about architecture so that we can handle not just the next country or regulatory regime that we have to comply with, but the next 10, the next 50.
Laurel: Well, and to follow up with a bit more of a specific example, how does R&D help improve manufacturing in the software supply chain as well as emerging technologies like artificial intelligence and the industrial metaverse?
Art: Oh, I love this one because this is the perfect example of there’s a lot happening in the technology industry and there’s so much back to the earlier point of applied curiosity and how we can try this. So specifically around artificial intelligence and industrial metaverse, I think those go really well together with what are Lenovo’s natural strengths. Our heritage is as a leading global manufacturer, and now we’re looking to also transition to services-led, but applying AI and technologies like the metaverse to our factories. I think it’s almost easier to talk about the inverse, Laurel, which is if we… Because, and I remember very clearly we’ve mapped this out, there’s no area within the supply chain and manufacturing that is not touched by these areas. If I think about an example, actually, it’s very timely that we’re having this discussion. Lenovo was recognized just a few weeks ago at the World Economic Forum as part of the global lighthouse network on leading manufacturing.
And that’s based very much on applying around AI and metaverse technologies and embedding them into every aspect of what we do about our own supply chain and manufacturing network. And so if I pick a couple of examples on the quality side within the factory, we’ve implemented a combination of digital twin technology around how we can design to cost, design to quality in ways that are much faster than before, where we can prototype in the digital world where it’s faster and lower cost and correcting errors is more upfront and timely. So we are able to much more quickly iterate on our products. We’re able to have better quality. We’ve taken advanced computer vision so that we’re able to identify quality defects earlier on. We’re able to implement technologies around the industrial metaverse so that we can train our factory workers more effectively and better using aspects of AR and VR.
And we’re also able to, one of the really important parts of running an effective manufacturing operation is actually production planning, because there’s so many thousands of parts that are coming in, and I think everyone who’s listening knows how much uncertainty and volatility there have been in supply chains. So how do you take such a multi-thousand dimensional planning problem and optimize that? Those are things where we apply smart production planning models to keep our factories fully running so that we can meet our customer delivery dates. So I don’t want to drone on, but I think literally the answer was: there is no place, if you think about logistics, planning, production, scheduling, shipping, where we didn’t find AI and metaverse use cases that were able to significantly enhance the way we run our operations. And again, we’re doing this internally and that’s why we’re very proud that the World Economic Forum recognized us as a global lighthouse network manufacturing member.
Laurel: It’s certainly important, especially when we’re bringing together computing and IT environments in this increasing complexity. So as businesses continue to transform and accelerate their transformations, how do you build resiliency throughout Lenovo? Because that is certainly another foundational characteristic that is so necessary.
Tech
The Download: covid’s origin drama, and TikTok’s uncertain future
Published
11 hours agoon
03/24/2023By
Drew Simpson
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
Newly-revealed coronavirus data has reignited a debate over the virus’s origins
This week, we’ve seen the resurgence of a debate that has been swirling since the start of the pandemic—where did the virus that causes covid-19 come from?
For the most part, scientists have maintained that the virus probably jumped from an animal to a human at the Huanan Seafood Market in Wuhan at some point in late 2019. But some claim that the virus leaped from humans to animals, rather than the other way around. And many continue to claim that the virus somehow leaked from a nearby laboratory that was studying coronaviruses in bats.
Data collected in 2020—and kept from public view since then—potentially adds weight to the animal theory. It highlights a potential suspect: the raccoon dog. But exactly how much weight it adds depends on who you ask. Read the full story.
—Jessica Hamzelou
This story is from The Checkup, Jessica’s weekly biotech newsletter. Sign up to receive it in your inbox every Thursday.
Read more of MIT Technology Review’s covid reporting:
+ Our senior biotech editor Antonio Regalado investigated the origins of the coronavirus behind covid-19 in his five-part podcast series Curious Coincidence.
+ Meet the scientist at the center of the covid lab leak controversy. Shi Zhengli has spent years at the Wuhan Institute of Virology researching coronaviruses that live in bats. Her work has come under fire as the world tries to understand where covid-19 came from. Read the full story.
+ This scientist now believes covid started in Wuhan’s wet market. Here’s why. Michael Worobey of the University of Arizona, believes that a spillover of the virus from animals at the Huanan Seafood market was almost certainly behind the origin of the pandemic. Read the full story.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 TikTok’s future in the US is hanging in the balance
Banning it is a colossal challenge, and officials still lack the legal authority to do so. (WP $)
+ TikTok CEO Shou Zi Chew was grilled by a congressional committee. (FT $)
+ He told lawmakers the company would earn their trust. (WSJ $)
+ Meanwhile, TikTok paid for influencers to travel to DC to lobby its cause. (Wired $)
2 A crypto fugitive has been arrested in Montenegro
Do Kwon has been on the run since TerraUSD stablecoin collapsed last year. (WSJ $)
+ Want to mine Bitcoin? Get yourself to Texas. (Reuters)
+ What’s next for crypto. (MIT Technology Review)
3 Twitter’s getting rid of its legacy blue checks
On the entirely serious date of April 1. (The Verge)+ The platform’s still an unattractive prospect for advertisers. (Vox)
4 Chatbots are having tough conversations for us
ChatGPT is adept at writing scripts for sensitive talks with kids and colleagues. (NYT $)
+ OpenAI has given ChatGPT access to the web’s live data. (The Verge)
+ How Character.AI became a billion-dollar unicorn. (WSJ $)
+ The inside story of how ChatGPT was built from the people who made it. (MIT Technology Review)
5 Jack Dorsey’s Block has been accused of fraudulent transactions
The payments company denied it, and claims it inflated its users numbers, too.(FT $)
+ Dorsey doesn’t have a track record of caring about this kind of thing. (The Information $)
6 Homeowners associations are secretly installing surveillance systems
The system tracks license plates and follows residents’ movements. (The Intercept)
7 Inside the tricky ethics of using DNA to solve crimes
A new database could help to protect users’ privacy. (Wired $)|
+ The citizen scientist who finds killers from her couch. (MIT Technology Review)
8 There’s plenty of reasons to be optimistic about the climate
Healthier, more sustainable diets are a good place to start. (Scientific American)
+ Taking stock of our climate past, present, and future. (MIT Technology Review)
9 TikTok keeps hectoring us
It seems we just can’t get enough of being aggressively told what to do. (Vox)
10 Don’t get scammed by a deepfake
CallerID can’t be trusted to protect you from rogue AI calls. (Gizmodo)
Quote of the day
“Wait, I need content.”
—TikTok fashion creator Kristine Thompson refuses to miss a content opportunity during a trip to the US Capitol to lobby against a potential TikTok ban, she tells the New York Times.
The big story
This sci-fi blockchain game could help create a metaverse that no one owns
November 2022
Dark Forest is a vast universe, and most of it is shrouded in darkness. Your mission, should you choose to accept it, is to venture into the unknown, avoid being destroyed by opposing players who may be lurking in the dark, and build an empire of the planets you discover and can make your own.
But while the video game seemingly looks and plays much like other online strategy games, it doesn’t rely on the servers running other popular online strategy games. And it may point to something even more profound: the possibility of a metaverse that isn’t owned by a big tech company. Read the full story.
—Mike Orcutt
We can still have nice things
A place for comfort, fun and distraction in these weird times. (Got any ideas? Drop me a line or tweet ’em at me.)
+ If underwater terrors are your thing, Joe Romiero takes some seriously impressive shark pictures and videos.
+ Try as it might, Ted Lasso’s British dialog falls wide of the mark.
+ Let’s have a good old snoop around some celebrities’ bedrooms.
+ Why we can’t get enough of those fancy candles.
+ Interviewing animals with a tiny microphone, it doesn’t get much better than that.