U.S. to Lend $1.9 Billion to Restart a Shuttered Nuclear Plant in Iowa

© Nick Rohlman/The Gazette, via Associated Press

© Nick Rohlman/The Gazette, via Associated Press
Leading chipmakers Samsung Electronics and Taiwan Semiconductor Manufacturing Co. announced plans to adopt ASML’s latest chipmaking machines in the next several years—and they also joined Intel in agreeing to a crucial technology change that could boost chip production on the new machines by 40 percent.
This signifies the semiconductor industry’s broader embrace of high NA EUV photolithography technology that can cost up to $400 million per machine and is only provided by the Dutch technology company ASML, Bloomberg reports. The technology would allow chip designers to implement even smaller features in potentially more powerful and efficient next-generation chips produced for AI data centers and consumer electronics such as smartphones, tablets, and laptops.
ASML’s EUV (extreme ultraviolet) lithography machines enable chipmakers to use powerful laser light to imprint patterns in silicon wafers, layer by layer, and gradually form the computer circuitry that makes silicon chips work. Compared to older deep ultraviolet lithography, EUV technology harnesses a more powerful source of light with a shorter wavelength to create even smaller features on silicon wafers.


© Michel de Heer | ASML
Leading chipmakers Samsung Electronics and Taiwan Semiconductor Manufacturing Co. announced plans to adopt ASML’s latest chipmaking machines in the next several years—and they also joined Intel in agreeing to a crucial technology change that could boost chip production on the new machines by 40 percent.
This signifies the semiconductor industry’s broader embrace of high NA EUV photolithography technology that can cost up to $400 million per machine and is only provided by the Dutch technology company ASML, Bloomberg reports. The technology would allow chip designers to implement even smaller features in potentially more powerful and efficient next-generation chips produced for AI data centers and consumer electronics such as smartphones, tablets, and laptops.
ASML’s EUV (extreme ultraviolet) lithography machines enable chipmakers to use powerful laser light to imprint patterns in silicon wafers, layer by layer, and gradually form the computer circuitry that makes silicon chips work. Compared to older deep ultraviolet lithography, EUV technology harnesses a more powerful source of light with a shorter wavelength to create even smaller features on silicon wafers.


© Michel de Heer | ASML
For years, TSMC has tried to avoid making public comments about its plans to use EUV lithography with a 0.55 numerical aperture optics, or High-NA EUV, because the company's developers had a good idea how to keep advancing process technologies without using $400 million scanners. However, TSMC cannot rely on Low-NA EUV systems forever, so this week the company announced plans to use High-NA EUV starting from 2030.
TSMC did not formally reveal which fabrication technology will be the first to adopt High-NA EUV, though the year 2030 points to a few candidates. What TSMC did say is that it expects the number of layers processed using High-NA EUV to eventually increase as its fabrication technologies become more complex, driven by increasing complexity of transistor architectures, which is probably an implication for more sophisticated implementations of gate-all-around (GAA) transistors as well as complementary field-effect transistors (CFETs) later on.
TSMC plans to start using High-NA EUV lithography tools for high-volume manufacturing in 2030 using conventional 6×6-inch photomasks. The company then plans to build a pilot line that uses 6×12-inch photomasks in 2031 with the goal of bringing 6×12-inch High-NA lithography systems into advanced node production by 2033.
High-NA EUV lithography tools can achieve an 8nm single-exposure resolution, as opposed to a 13nm single-exposure resolution offered by today's Low-NA EUV litho systems. However, when used with conventional 6×6-inch photomasks, High-NA EUV scanners have only half the exposure field of their Low-NA counterparts, which creates challenges for manufacturing very large dies. As a result, chipmakers building massive AI accelerators must either stitch multiple exposure fields together or adopt multi-chiplet designs, two approaches that have their own other challenges, such as tool productivity and power consumption. To circumvent the 6×6-inch photomask limitations, TSMC is working with ASML to set the stage for 6×12-inch photomasks.
Changing the size of photomasks is not a trivial endeavor as it requires changing everything from EDA software to tools that produce and write masks as well as systems that handle them, which essentially means that the entire industry must work on this change. ASML seems to be optimistic about the transition as it is supported not only by Intel and TSMC, but also by Samsung.
"We expect the adoption of High NA EUV to increase progressively along the device scaling roadmap, first using current 6-inch masks and then further supported by 12-inch masks, which enable greater scanner productivity and allow the industry to meet the demand for smaller, faster and more energy-efficient chips," said Christophe Fouquet, president & CEO, ASML. "We are pleased by the strong initial support of semiconductor manufacturers, mask suppliers and partners for this initiative."

Perhaps the biggest intrigue about TSMC's usage of High-NA EUV lithography is which process technology will be the first to use the new systems. Based on what we know about TSMC's roadmap, A10 or A11 (1/1.1nm-class) seems to be by far the strongest candidates to use High-NA EUV scanners for the most critical layers. TSMC's latest strategy separates its roadmap into annual client-oriented nodes (N2, N2P, N2X, A14, A13) and roughly biennial high-performance nodes (A16 in 2027, then A12 in 2029). The company has already confirmed that A12 and A13, due in 2029, will continue to rely on conventional EUV lithography.
Since A13 is an optical shrink of A14 that increases transistor density by only 6%, with performance and power improvements yet to be disclosed, its successor in 2030 will likely have to deliver considerably more substantial gains. It is therefore reasonable to expect A13’s successor — whether it is called A11 or A10 — to adopt more advanced lithography and/or TSMC's 3rd Generation nanosheet GAA transistors to deliver significantly higher transistor density as well as meaningful performance and power improvements over its predecessor. Yet, we are of course speculating.
A 52-year-old Belgian-Chinese man has been arrested on suspicion of espionage for allegedly transferring specialized semiconductor intellectual property and trade secrets from Belgian chipmaker BelGaN, where he worked, to China. According to an AP News report citing details released by Belgium's federal prosecutor’s office on September 7, the Beijing-born Belgian resident was taken into custody on May 10 at a Brussels airport while attempting to board a flight to China.
The suspect — identified by Belgian media only as H.L. — previously held a senior research position at the now-defunct BelGaN, which specialized in gallium nitride (GaN) semiconductors. Investigators suspect he was concurrently leading a Chinese chip firm, GanKool, which was developing the exact same specialized gallium nitride chip technology as BelGaN. The company was established only a few months after H.L. joined BelGaN and was financed by a Chinese investment fund. The suspect now faces charges of industrial espionage, membership in a criminal organization, misuse of company assets, and unlawful disclosure of business secrets.
The espionage allegations emerged from an investigation into BelGaN's collapse. When the company collapsed into bankruptcy in the summer of 2024, Belgian authorities launched a routine financial probe into the failure. Investigators eventually uncovered highly unusual corporate activity. Instead of trying to keep BelGaN financially viable, the managers and the researcher may have actively tried to make the Belgian company “disappear” after transferring its proprietary knowledge. This led to further investigations that revealed GanKool’s existence and its connections to BelGaN.
In addition to the researcher’s role, the investigators also found evidence linking BelGaN's last CEO, a Chinese national, to the same Chinese rival. The prosecutors have said a second suspect remains at large, with Belgian media speculating that the CEO is the suspect.
The incident highlights the increasingly intense competition over technology and intellectual property in the semiconductor industry, particularly as China pushes to reduce its reliance on foreign chip technology while the U.S. continues to restrict its access to advanced processors and semiconductor manufacturing equipment. Just last month, South Korean court documents accused Chinese memory maker CXMT of using a detailed “Project Hefei” roadmap to obtain Samsung's proprietary DRAM technology, including a 620-step process recipe. A former Samsung engineer has already been sentenced to seven years in prison over the scheme.
Intel announced on Monday that it had processed more than one million 300-mm wafers using its High-NA EUV scanners, less than two and a half years after its first tool was assembled. For now, the company intends to use industry-standard 6-inch photomasks, which can expose 26×16.5 mm half-fields and therefore require field stitching for larger chips. However, Intel is also working on larger 6×12-inch photomasks that would enable High-NA EUV scanners to expose full 26×33 mm fields without stitching.
One million High-NA wafers
Intel's one million wafers figure includes wafers processed during tool installation and certification, R&D, and production. Earlier this year, Intel certified using High-NA EUV scanners for its 18A process technology, so right now these tools are used to make some of Intel's Panther Lake processors. Intel currently has two ASML Twinscan EXE:5000 tools and at least one EXE:5200B scanner. As of late February 2025, Intel processes around 30,000 wafers using its High-NA EUV tool, so going from 30,000 wafers by February 2025 to over a million by September 2026 is an enormous increase in cumulative High-NA utilization.
Since Intel's fleet expanded from two EXE:5000 systems to three and now includes the much faster EXE:5200B, the million-wafer milestone is really a fleet and process-maturity milestone that Intel has achieved first in the industry. What makes the company's milestone even more important is that ASML announced this April that all of the High-NA EUV scanners shipped by then processed over 500,000 wafers which achieving over 80% availability, which means that Intel has now processed more wafers using High-NA tools than the rest of the industry combined.
Conventional 0.33-NA EUV has 4X magnification in both directions, enabling the familiar 26×33 mm exposure field with traditional 6-inch photomasks. However, 0.55-NA EUV uses anamorphic 4X/8X magnification, so the same 6×6 mask can provide only approximately 26×16.5 mm on the wafer. As a result, large dies that fit within a conventional 26 × 33 mm EUV field must be exposed as two half-fields using High-NA EUV, which is called stitching. While stitching is a workable near-term solution, it has several drawbacks.

Firstly, it greatly reduces throughput from 175 wafers per hour to 125 wafers per hour on an EXE:5200B. Secondly, chip designs must account for stitching and must be developed with stitching in mind, which means less floor planning freedom. Thirdly, the two exposures must be aligned extremely precisely so that features crossing the stitching boundary connect properly. Even a tiny misalignment can distort lines and vias, or break interconnects, which potentially creates defects and reduces yields, which will be a particularly costly problem for large CPU and GPU dies.
To avoid using stitching, the industry — led by Intel — plans to shift to larger 6×12 masks, which will enable a 26×33 mm full field in one exposure. While this looks easy on paper, make the mask twice as long, changing the mask represents an enormous ecosystem change.
Moving from 6×6-inch to 6×12-inch photomasks would require substantial changes across the existing mask ecosystem, including mask blanks and deposition, etching, inspection and metrology, cleaning, pellicles, mask writers, and mask handling systems. Crucially, High-NA EUV scanners would also have to be modified or redesigned to accommodate the larger masks, which will make the transition a major retooling effort across the semiconductor supply chain. While neither ASML nor Intel confirmed that existing or planned High-NA EUV scanners can be modified to handle larger masks, all of the future High-NA EUV scanners to be launched before and after 2033 are designed around 6×6-inch reticles and stitching, according to ASML's roadmap.
It remains to be seen whether the industry moves on to larger 6×12-inch photomasks, but Intel appears to be the main evangelist for changing the mask standard that has defined projection lithography infrastructure for decades. If the effort comes to fruition, then Intel will likely have a considerable first-mover advantage over its industry peers because it will define and set the standard for the projection lithography industry for decades to come, an advantage that is hard to overestimate.
Machine vision is becoming a practical part of Apple’s U.S. factory training strategy, giving smaller manufacturers access to techniques that help production systems inspect what they make. The recent opening of the Houston manufacturing training center extends that effort into a working electronics production campus, while courses developed with Michigan State University introduce businesses to image analysis, machine learning, and automated quality checks.
For a manufacturer, the attraction can be straightforward: finding a missing component before a product leaves the line, identifying a surface defect before more material is added, or recognizing an assembly error while it can still be corrected cheaply.
These are manageable problems with measurable costs. They also offer an accessible starting point for businesses interested in AI but unsure where it belongs inside their operations.
The Apple Manufacturing Academy’s current course catalog includes Machine Learning with Vision, a course introducing machine learning and deep learning through practical activities using Apple devices. It sits alongside instruction in automation, quality fundamentals, manufacturing data, maintenance, and process improvement.
That placement gives the subject useful context. A camera can produce an image, but a factory needs a decision: accept the part, flag it for review, check its position, or investigate a process that may be drifting out of tolerance.
Machine vision connects image capture to those operational decisions. Some applications use established rules to measure dimensions or identify patterns. Others use machine learning to recognize acceptable variations and defects that are difficult to describe through fixed rules.
The academy’s public description confirms introductory activities with Apple devices. It does not identify a particular iPhone, Mac configuration, software framework, or industrial camera package that every participant must use.
For smaller businesses, the educational value is learning how to define an inspection problem before purchasing equipment. A company that understands what it needs to detect can have a much more productive conversation with an automation supplier.

Apple opened its 20,000-square-foot Advanced Manufacturing Center on August 13. According to the Houston opening announcement, the free program offers small and medium-sized businesses instruction in advanced manufacturing, interactive labs, and experience with representative production equipment.
The first cohort worked with machine learning for quality control and advanced automation. Participants also explored production challenges, used a holographic table, and assembled and laser etched a product.
The location adds context: the center occupies the same Houston facility that builds advanced AI servers and is scheduled to begin Mac mini manufacturing this year. Its teaching program includes final assembly principles and design considerations for printed circuit board assembly.
Houston is the company’s second U.S. manufacturing learning site, following the Detroit academy established with Michigan State University in August 2025. By the Houston opening, Apple said the academy had helped nearly 1,000 workers, students, and entrepreneurs.
That figure measures people reached through the program. It should not be interpreted as 1,000 factories already operating AI inspection systems.

Imagine a small business assembling control panels. Each panel needs the same set of connectors, positioned correctly before the enclosure closes. A machine vision station could check for a missing connector while the assembly remains accessible.
The value comes from catching that error at the right moment. Discovering it after packaging creates extra handling and rework. Discovering it after delivery may involve a return, a service visit, and an unhappy customer.
A useful pilot would therefore begin with that single inspection point. The business could compare the existing process with the assisted one, recording missed defects, unnecessary rechecks, inspection time, and the cost of correcting mistakes.
The camera is only part of the exercise. Workers must agree on what constitutes a defect, collect representative examples, and decide what happens when the result is uncertain.
Those decisions draw heavily on experience already present on the factory floor. An operator may know that an unusual mark is harmless, while a barely visible change in alignment predicts a later assembly problem. Turning that knowledge into consistent inspection criteria is valuable work in its own right.
These are examples of how manufacturers could apply the training, rather than projects Apple has announced for individual participants.
A demonstration can look impressive when every part arrives clean, centered, and evenly lit. Production introduces variation: different surface finishes, reflections, changing positions, and batches that look slightly different while remaining within specification.
The inspection system must distinguish those acceptable differences from actual failures. Otherwise, it can reject good products or allow defective ones to continue.
Industrial vision supplier Cognex’s deployment guidance recommends validating systems across product models, defect types, and manufacturing lines. It also describes an approach in which automated inspection handles preliminary checks while experienced inspectors review borderline cases.
For a business learning machine vision, that offers a realistic way to introduce the technology. Employees can compare its decisions with their own, investigate disagreements, and determine whether the results justify extending its role.
An inspection system also needs an owner after installation. Someone must understand when a product change requires new examples, when equipment needs attention, and when performance has slipped. Training that responsibility into the workforce helps prevent an initially successful project from becoming an unreliable fixture.
The academy’s reach extends beyond companies producing consumer electronics. In its May update on participating manufacturers, Apple highlighted Block Imaging, a Michigan business that services and refurbishes medical imaging equipment.
The company hosted visitors during the academy’s Spring Forum and described applying techniques learned from Apple engineers and Michigan State experts to its operations. Apple reported that the academy had supported more than 150 American businesses at that point.
The announcement did not identify a particular machine vision deployment at Block Imaging or publish a percentage improvement attributable to visual inspection. It does provide an example of the training reaching businesses whose work differs substantially from building an iPhone.
That wider participation could benefit Apple indirectly. More capable domestic manufacturers can support equipment maintenance, tooling, components, and specialized production services. Participation itself does not establish a supplier relationship, but the skills can be used across customers and industries.
The academy’s participation information says eligible small and medium-sized U.S. manufacturers can take courses free of charge. Online learning includes on-demand materials and interactive modules, while in-person sessions add workshops and more personalized instruction.
Digital badges are available for different topics, and completed course materials remain accessible for review. That gives a business room to train an employee around existing production commitments and return to the material when an actual project begins.
Free instruction does not remove the eventual cost of cameras, lighting, integration, maintenance, or employee time. It can help a manufacturer understand those costs before committing to a system.
The first proposal brought back to the factory might be modest: inspect one recurring defect at one station and record the results for several weeks. A supervisor can then weigh the time saved against the extra reviews, maintenance, and mistakes. That is the kind of evidence a business owner can use when deciding whether to install a second station.

The post Machine Vision Joins Apple’s U.S. Factory Training Push appeared first on AppleMagazine.

© Jason Gulley for The New York Times
Intel is set to raise $19.7 billion by selling new common stock in a bid to finance the building out of new production capacity, the development of next-generation leading-edge process technologies like 14A and others, and day-to-day operations. While the company does not assign money to a particular project, Intel needs to build capacity to land orders from large external clients, so capacity expansion will likely be a priority. According to Bloomberg, the share sale attracted $100 billion in demand.
Intel will sell 210,526,315 shares for $95 apiece through an underwritten public offering. In addition, participating banks have 30 days to acquire as many as 31,578,947 more shares at the same $95 price, minus applicable underwriting discounts. Should they exercise all their options, Intel could sell approximately 242.1 million shares altogether and increase the proceeds to roughly $23 billion. Without the additional shares, Intel expects net proceeds of approximately $19.7 billion after underwriting discounts, commissions, and estimated expenses. The transaction is scheduled to close on August 12, 2026.
Intel's market capitalization increased from roughly $90 billion last August to $491 billion at press time, so the time is right to sell some shares and raise some much-needed cash, as the company must compete against giants like TSMC and Samsung, which spend tens of billions of dollars every year on new fabs and advanced process technologies. Meanwhile, Intel's capitalization reached its all-time high of $673 billion on June 20, 2026.
Intel has not assigned the money it is going to raise to particular projects and says the capital can be used across the business, including for capital expenditures and working capital. The company is currently ramping up its Fab 52 in Arizona and is on track to start using adjacent Fab 62 when it needs to. In addition, the company still has to build its fab complex in Ohio, which is expected to cost over $100 billion when fully built, so it badly needs money.
In its risk disclosures, the company specifically mentioned Intel 14A — which is due to enter mass production in 2028 — and other advanced process technologies, manufacturing expansion required to support them, and the need to secure design wins and volume commitments from major external foundry customers. While Intel does caution that these long-term investments amounting to tens of billions may not generate adequate returns, it is impossible to land sizeable contracts from external customers without having production capacity readily available.
Interestingly, Intel also mentioned alternative financing arrangements, government grants, and the U.S. government's significant equity position in the company among relevant factors.
House Select Committee on China Chairman John Moolenaar has written a letter that demands the U.S. government enforce an existing export control measure designed to prevent Chinese companies from getting advanced chips produced by contract chipmakers like TSMC or Samsung Foundry.
When Joe Biden was about to leave the office in early 2025, he signed a law that required chipmakers to determine their end customers in a bid to prevent contract manufacturers from unknowingly fabricating chips using American technologies for companies that served as intermediaries for restricted organizations tied to the Communist Party or the People Liberation Army. The regulation was introduced after it was discovered that chips made by TSMC for China-based Sophgo were actually Huawei's Ascend 910B AI accelerators. However, the Trump administration announced in May 2025 that it would not enforce this semiconductor regulation known as the 'Foundry Due Diligence Rule,' which naturally created uncertainty about whether the foundry-focused requirements would be actively implemented at all.
"The announcement created ambiguity as to whether front-end fabricators like TSMC can export unpackaged advanced dies to non-approved designers located outside of China, without performing the due diligence specified in the Foundry Due Diligence Interim Final Rule (IFR)," the letter reads. "These exports enabled Huawei to obtain millions of controlled Ascend logic dies from TSMC, using its front company, Sophgo in 2023 and 2024."
Before the adoption of the 'Foundry Due Diligence Rule,' Chinese companies and/or their intermediaries could misrepresent chip specifications and end users when placing orders with TSMC or other foundries, which allowed restricted devices to be made despite U.S. export controls. Under the rule, foundries and OSAT providers exporting chips produced on 14/16nm-class process technologies or more advanced nodes must presume those devices qualify as controlled AI processors subject to a global licensing requirement, unless they meet an exemption. According to the letter, foundries have generally complied with these requirements since the rule took effect. As a result, Moolenaar essentially asks the government to enforce the existing controls rather than introduce stricter export controls.
Moolenaar believes that the Commerce Department's Bureau of Industry and Security could clarify its current stance on the Foundry Due Diligence Rule in two ways:
Either approach would eliminate conflicting interpretations and reinforce enforcement of existing export controls, Moolenaar believes.
Apple used to be among a few companies willing to buy memory and other components worth billions of dollars under long-term supply contracts at fixed prices. But the artificial intelligence era represents a new reality with new purchasing champions, marking a tectonic shift in the high-tech world. Alphabet, Microsoft, Meta, and Amazon have purchase commitments totaling about $2 trillion, and a significant portion of these commitments are for memory, according to estimates by analyst Claus Aasholm. While the commitments are approximate, span many years, and should be generally taken with a grain of salt, they still reflect the direction the industry is moving.
Combined purchasing commitments from the four major hyperscalers shown in the chart —Amazon, Alphabet, Meta, and Microsoft — reached nearly $2 trillion by Q2 2026, with Alphabet and Microsoft accounting for the overwhelming majority of the total.
The rapid expansion suggests several major findings. Firstly, the AI infrastructure race is accelerating, not stabilizing. Secondly, AI infrastructure investments are driven by a handful of hyperscale cloud service providers (CSPs) whose long-term procurement commitments now vastly exceed those of traditional consumer electronics companies such as Apple.
Thirdly, memory has become a strategic asset — perhaps a competition weapon — rather than a commodity. Fourthly, suppliers of memory — both 3D NAND and DRAM — are gaining pricing power. Finally, demand for memory will likely drive major capacity expansion at Micron, Samsung, and SK hynix, even though so far these companies have been exceptionally disciplined about their capacity investments.
Google shows by far the most aggressive increase in purchasing commitments, rising from roughly $140 – $150 billion in Q3 2025 to around $811 billion by Q2 2026 (though these are total purchase commitments by Alphabet, not specifically memory purchase commitments), while Microsoft follows a similar trajectory and reaches approximately $678 billion in total obligations, which includes, but is not limited to memory.
Meta is also ramping commitments substantially to around $349.3 billion (again, these are total commitments), whereas Amazon increased its commitments more gradually to roughly $130 billion. By contrast, Apple — which makes the world's most popular smartphone, and which was the largest consumer of memory just a couple of years ago — remains almost flat throughout the period at approximately $57 billion (of which $56.2 billion is payable within 12 months). Apple's commitments fall well short of Nvidia's commitments of $119 billion.
Memory suppliers used to buzz around Apple like fruit flies, but now they have discovered larger commitments.Apple's purchasing commitments have not changed, suggesting a reluctance to follow the new market rules.https://t.co/0pRbk8aYVJ pic.twitter.com/t2VNm7uw1dAugust 6, 2026
Again, we are talking about total purchase commitments, which include foundry capacity, 3D NAND, and DRAM memory, but are not limited to them. Alphabet, Amazon, Meta, and Microsoft all build custom silicon and custom servers, so a significant portion of these commitments is to various EMS providers.
While $1.968 trillion of purchase commitments for memory and storage alone would be an absurdly large amount of money, a huge portion of these commitments consists of contract manufacturing obligations as well as memory chips. This suggests that the foundry, 3D NAND, and DRAM markets are entering a new phase in which hyperscalers are willing to make vastly larger forward purchasing commitments than traditional consumer-electronics companies, giving suppliers a strong incentive to prioritize customers prepared to secure future capacity on that scale.
While Claus Aasholm's chart is explicitly dedicated to memory, it does describe total purchase commitments of tech giants, so the chart can reasonably be read as evidence that memory and capacity at TSMC, Samsung Foundry, and GlobalFoundries are becoming a strategic asset rather than merely another component to procure at the best available price.
AI infrastructure requires enormous quantities of AI accelerators, DRAM (including HBM), and 3D NAND. Meanwhile, the supply of high-end memory (HBM) is constrained by fab capacity at major DRAM makers, whereas the supply of AI accelerators is constrained by both wafer capacity and foundries and packaging capacity at foundries and their OSAT partners. As a result, hyperscaler CSPs have an incentive to lock in supply years ahead, even if doing so requires exceptionally large purchasing commitments.
That also changes the relationship between semiconductor suppliers and their customers. In theory, a company willing to guarantee hundreds of billions of dollars of future purchases can effectively help underwrite expansions of foundry, memory, and advanced packaging capacity and, in return, secure priority access to scarce products and future process technologies. In reality, TSMC can well afford capacity expansion using the money it gets from hyperscalers and give priority to its largest customers. In this environment, access to DDR5, HBM, and 3D NAND memory becomes part of the competitive advantage rather than merely a procurement exercise.
This is also what makes Apple's position in the graph interesting: its purchasing commitments barely move while those of Alphabet, Amazon, Meta, and Microsoft surge. If the trend continues, Apple may remain one of the world's largest semiconductor buyers in absolute terms, but the question is whether it will be among the key customers that foundries, memory makers, and OSATs plan their future capacity expansions.
Perhaps the most interesting takeaway of the findings revealed by long-term purchase commitments is that the industry's center of gravity appears to have shifted.
During the smartphone era, foundries (well, TSMC has won) and memory suppliers often competed aggressively for Apple's business because of its enormous purchasing power. Today, hyperscalers building AI infrastructure are making purchasing commitments that dwarf those of traditional CE companies like Apple, which may well represent a strategic inflection point akin to the one Andy Grove described in his 'Only the Paranoid Survive' book.
Will this tectonic shift result in prioritization of customers capable of enabling future capacity expansions through massive long-term purchase agreements, or will foundries and memory makers remain more or less disciplined with their capacity expansions so as not to lose a lot when demand declines? This is a question that has yet to be asked.
In any case, the AI megatrend has transformed semiconductors — from foundries to advanced packaging and from DDR5 to HBM4 — into strategic assets that can no longer be treated as ordinary components procured on demand. And this is something that will continue in the long run.
Most megafactories usually take years to go from conceptualization to construction, but a recent drone flyover of the Terafab showed that progress has already started on the ground less than five months after Musk unveiled it. However, the video doesn’t do justice to the true scale of its footprint, so X user Nic Cruz Patane created a visualization to help us understand how large the chip manufacturing facility is.
Terafab approximate size comparison between Giga Texas, the Pentagon, Apple Park, and the Mall of America.There has never been a building this large. Elon Musk says it will be the most valuable building by far. pic.twitter.com/GCmpfeduJnAugust 6, 2026
The site will reportedly have a floor space of at least 100 million square feet, making it larger than Giga Texas (10 million sq. ft), the Pentagon (6.6 million sq. ft), Apple Park (2.82 million sq. ft), and the Mall of America (5.6 million sq. ft), combined. This also makes it significantly larger than New Century Global Center in Chengdu, China, with has an interior space of “just” 18.9 million sq. ft.
While this might seem like an absurd amount of space for chip manufacturing, it appears that the Terafab will need it because it’s going to be more than just a chip fab making AI processors — instead, it will be an all-in-one facility that will produce logic and memory chips, as well as have lithography, packaging, and testing under one roof.
Elon Musk started talking about building his own chip manufacturing facility in late 2025 and officially announced the project in March of this year. The reasoning behind this project is that both SpaceX and Tesla will require at least 1TW of compute, which is more than ten times that current global chip supply.
Nvidia CEO Jensen Huang warned that a project like this will be an “extremely hard” challenge, but it appears that Musk is willing to put his massive resources on the line for this. Intel CEO Lip-Bu Tan even said that he can “think of no better partner than Elon Musk” to explore “unconventional” ways of chip manufacturing.
Musk is no stranger to both leading and funding projects that otherwise seemed impossible. Although he did not found Tesla, his investment and leadership in the company led it to become an industry trailblazer in EVs. He fundamentally changed commercial space travel with SpaceX; something that used to be the purview of NASA and other national governments, and he also broke a record when he set up 100,000 Nvidia H200 GPUs in just 19 days back in 2024 — a process which Huang said usually takes four years.
It seems that Terafab is Musk’s biggest project to date, which is estimated to require up to $119 billion in investments. But even though it seems that this project already has a secure customer base in SpaceX and Tesla, the former acknowledges that there’s a risk that this ambitious megafactory may not be successful.
A new study is challenging one of the most persistent assumptions in space manufacturing: that building advanced composite structures beyond Earth is simply a matter of moving terrestrial production methods into orbit. Research published in npj Advanced Manufacturing examines how two defining features of the space environment—reduced gravity and reduced atmospheric pressure—can alter the manufacture of carbon-fiber-reinforced structures, materials widely regarded as essential for future spacecraft, habitats and large orbital infrastructure.
Carbon-fiber-reinforced polymers, commonly known as CFRPs, combine strong, lightweight carbon fibers with a polymer resin that binds the fibers into a rigid composite. Their exceptional strength-to-weight ratio makes them attractive for launch vehicles and spacecraft, where every kilogram matters. Yet manufacturing these materials is a delicate process. Fibers must be positioned accurately, resin must penetrate the reinforcement evenly, and gases or trapped voids must be removed before the material cures. On Earth, gravity and atmospheric pressure quietly assist—or complicate—each of these steps.
In space, those familiar forces change dramatically. Reduced gravity can alter how liquid resin flows through a carbon-fiber structure, while low pressure can encourage dissolved gases and volatile compounds to expand or escape. Together, these effects may influence impregnation, consolidation, curing and the final mechanical performance of a composite part. The new work by R. Strecker, T. Rieger, A. Tabelander and colleagues focuses on understanding these interactions before manufacturers attempt to produce increasingly complex structures in orbit.
The central issue is void formation. A composite that appears solid can contain microscopic pockets of air or vapor between its fibers. These voids interrupt the transfer of stress through the material and can reduce strength, stiffness and fatigue resistance. During conventional manufacturing on Earth, pressure is often applied to compress the layers and drive out unwanted gases while resin fills the spaces between fibers. In a reduced-gravity environment, however, buoyancy-driven movement is weakened, and the pathways through which bubbles travel may change. A bubble that would rise and escape during terrestrial processing could remain trapped inside the structure.
Pressure reduction introduces another layer of complexity. Lower ambient pressure can help remove volatile substances and trapped gases, but it can also cause dissolved gases in the resin to expand. If the resin is not sufficiently mobile, expanding bubbles may become locked in place as the material cures. The balance between degassing and bubble growth therefore becomes a critical manufacturing parameter. The study’s importance lies in treating pressure and gravity not as isolated variables, but as environmental conditions that can interact throughout the production cycle.
Resin flow is equally important. Carbon-fiber reinforcements are made from tightly packed filaments, creating narrow channels through which resin must travel. On Earth, gravity can influence resin distribution, especially in vertically oriented or large components. In reduced gravity, capillary forces and surface tension become relatively more significant. These forces can help draw resin into small spaces, but they may also create uneven wetting patterns depending on the fiber architecture, resin viscosity and processing orientation. Such changes could affect whether a component cures uniformly from its surface to its interior.
The findings carry implications far beyond laboratory samples. Future space manufacturers may seek to fabricate beams, pressure-vessel elements, antenna supports, solar-array components and repair patches directly in orbit. Producing these parts where they are needed could reduce the need to launch bulky finished structures from Earth. It could also enable designs too large to fit inside a rocket fairing, including trusses and habitation modules assembled gradually in space. But the economic and engineering promise of this approach depends on reliable quality control. A component that looks flawless may still fail if its internal fiber alignment, resin distribution or void content is inconsistent.
The research also highlights why manufacturing hardware designed for Earth cannot automatically be considered space-ready. Pumps, heaters, vacuum systems, molds and curing chambers must all operate under conditions in which heat transfer, fluid movement and gas removal behave differently. Temperature control is particularly important because resin viscosity changes strongly with temperature. A resin that flows readily during terrestrial processing might move more slowly or unevenly in orbit, while excessive heating could accelerate curing before impregnation is complete. Space-based production systems will therefore need carefully coordinated control of pressure, temperature, resin chemistry and processing time.
For engineers, the study provides a foundation for developing manufacturing protocols tailored to reduced-gravity environments. Instead of copying Earth-based workflows, future systems may need staged pressure changes, specialized fiber preforms, modified resin formulations or active bubble-management techniques. Sensors could monitor pressure, temperature and resin flow in real time, allowing automated systems to detect defects before a part is finished. Non-destructive inspection, including imaging and ultrasonic testing, will also be essential for verifying the integrity of components that cannot easily be returned to Earth for analysis.
The broader message is that space manufacturing is not merely an extension of industrial production—it is a new manufacturing regime governed by different physics. Understanding how gravity and pressure reshape the behavior of fluids, gases and composite materials could determine whether orbital fabrication remains a promising experiment or becomes a practical industrial capability. By examining the production of carbon-fiber-reinforced structures under space-relevant conditions, Strecker, Rieger, Tabelander and their colleagues are helping identify the hidden variables that will decide how safely and efficiently humanity can build beyond Earth.
Subject of Research: Effects of reduced gravity and reduced atmospheric pressure on the in-space manufacturing of carbon-fiber-reinforced structures
Article Title: Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures
Article References: Strecker, R., Rieger, T., Tabelander, A. et al. Effects of reduced gravity and reduced atmospheric pressure on in-space manufacturing of carbon-fiber-reinforced structures. npj Adv. Manuf. 3, 26 (2026). https://doi.org/10.1038/s44334-026-00104-w
Image Credits: AI Generated
DOI: https://doi.org/10.1038/s44334-026-00104-w
Keywords: in-space manufacturing, carbon-fiber-reinforced polymers, reduced gravity, reduced atmospheric pressure, composite materials, resin flow, void formation, space technology
SpaceX and Tesla on Thursday formally unveiled plans for the initial phase of their Terafab project. The first stage of the plan — which is expected to use Intel's 14A process technology — is expected to require $16.8 billion in investment, while the completed campus is planned to encompass more than 100 million square feet of manufacturing space. The massive semiconductor manufacturing complex will be built in Grimes County, Texas, at a location that belongs to SpaceX.
According to SpaceX and Tesla, their combined demand for semiconductors is projected to exceed 1 terawatt (TW) of compute per year, which significantly exceeds today's global supply. In fact, SpaceX, Tesla, and xAI already consume a significant portion of contract chipmaking services available today, and can potentially justify building a dedicated fab that will exclusively serve Elon Musk's companies. In particular, Terafab is envisioned to exclusively produce AI inference processors for Tesla Optimus humanoid robots and Cybercab autonomous vehicles, as well as 'high-power' processors intended for SpaceX's space-based data centers. Meanwhile, SpaceX and Tesla have not disclosed when their combined demand for compute per year will hit the 1 TW benchmark.
Unlike a conventional semiconductor fab, Terafab is envisioned as a vertically integrated manufacturing campus where advanced logic devices, memory chips, packaging, and testing operations are housed together. Normally, logic and memory are produced at different fabs using different process technologies, whereas packaging and testing services are performed at different facilities. However, the companies believe that consolidating logic, memory, packaging, and testing in one location will not only shorten the production cycle but will also shorten time-to-yield by enabling faster iterative improvements.
Given the description and the goal of the project, this will be a massive facility. Yet, SpaceX and Tesla have said little about its capabilities; The only thing they have disclosed is that the current facility in Grimes County will feature '100 million square feet of manufacturing space.' The 100 million square feet (9.3 million square meters) figure immediately stands out because it is far beyond anything ever announced for a semiconductor manufacturing facility. For example, the total area of Samsung's Pyeongtaek campus is approximately 2.89 million square meters, or 31.1 million square feet. A single Samsung fab occupies about 120,000 square meters, or 1.29 million square feet. However, the key wording is important: '100 million square feet of manufacturing space' does not mean 100 million square feet of cleanroom space.
Based on an image published by SpaceX, the Terafab facility will occupy four massive buildings. It is unclear whether these buildings will be four phases of the project (i.e., logic, memory, and packaging will be made under one roof) or will serve different purposes (i.e., one building makes logic, another produces memory, yet another does testing and packaging). In any case, when fully built, Terafab will be a massive semiconductor production campus that will require significantly more than $16.8 billion.
The announcement claims that the Terafab facility will employ at least 3,000 people, and that between 60% and 80% of them will be Grimes and nearby Brazos County residents.
Terafab is expected to use water from Gibbons Creek Reservoir instead of local groundwater and feature on-site wastewater treatment as well as water recycling and conservation measures.
The announcement follows Tesla's groundbreaking earlier this year on a research semiconductor facility at the North Campus of Tesla's Giga Texas campus, which the companies describe as a precursor to Terafab.

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Researchers have demonstrated an unusual new form of 3D printing capable of building entirely new structures directly onto—and even around—existing objects in just seconds. In a series of experiments, the technique printed microscopic fluid channels around embedded glass spheres, fabricated precision gears onto polished steel rods, and even built a miniature lens directly onto the surface of a working LED.
The breakthrough, described in a paper published in Nature Communications, relies on an advanced form of tomographic volumetric additive manufacturing (TVAM) that allows researchers to “overprint” new structures onto components made from a wide range of materials, including metal, glass, and preassembled microfluidic components. Instead of constructing objects layer by layer like conventional 3D printers, the system projects precisely calculated light patterns into a rotating vat of photosensitive resin, causing an entire three-dimensional object to solidify almost simultaneously within seconds.
The advancement could represent a major step toward making volumetric 3D printing practical in real-world manufacturing environments, where new components often must be integrated with parts that already exist.
It additionally highlights the growing sophistication of computational techniques that can accurately model how light behaves as it passes through materials that absorb, scatter, refract, or reflect light.
“Tomographic Volumetric Additive Manufacturing (TVAM) is a light-based 3D printing technique capable of producing centimeter-scale objects within seconds,” researchers write. “This work demonstrates a broad range of overprinting scenarios, where new structures are directly printed onto or around pre-existing components made from different materials.”
Unlike conventional stereolithography or fused-filament printers that gradually build an object from one direction, tomographic volumetric printing projects hundreds of two-dimensional light patterns into a rotating container filled with liquid resin.
As the resin accumulates enough light energy, the desired three-dimensional structure appears almost simultaneously throughout the volume rather than emerging layer by layer. The approach can produce centimeter-scale objects in mere seconds.
However, until now, one of the technology’s greatest challenges has been accurately calculating how light travels through complicated environments.
Existing objects inside the resin can block, scatter, or bend incoming light, degrading print quality. Conventional models often assume these objects are either completely transparent or completely opaque. This is an oversimplification that breaks down in realistic manufacturing scenarios.
To overcome those limitations, the team developed an upgraded computational framework called Dr.TVAM. Built upon physically based ray tracing, the software models how light propagates through materials with different optical properties while simultaneously optimizing the projected light patterns needed to create the desired object.
Because the framework accounts for reflection, refraction, scattering, absorption, and arbitrary container shapes, it can successfully print structures around components that previously would have interfered with the process.
One of the study’s most compelling demonstrations involved manufacturing microfluidic channels directly inside sealed, preassembled square chambers. Microfluidic devices contain microscopic channels that transport tiny volumes of liquids and are widely used in biomedical research, diagnostics, and organ-on-a-chip systems.
Instead of fabricating the channels separately and assembling the device afterward—a process that can introduce contamination or leaks—the researchers successfully printed perfusable channels directly inside an already assembled chamber while accounting for inlet and outlet structures that would otherwise obstruct the light.
They demonstrated straight channels, branching networks, and spiral geometries capable of carrying fluid after fabrication. Supplementary demonstrations showed colored dye successfully flowing through the finished structures.
The researchers then pushed the concept even further by creating what they describe as context-aware printing. In this experiment, they embedded two tiny glass spheres inside a hydrogel-filled chamber, rapidly determined each sphere’s three-dimensional position using two camera images, and generated customized print patterns in less than a minute.
Those optimized projections produced hollow channels connecting each sphere to inlet and outlet ports while simultaneously printing protective cavities around the spheres themselves. The complete workflow—from detecting the embedded objects to generating printing instructions and fabricating the final structure—required less than three minutes.
That ability could eventually enable highly customized biomedical manufacturing, where printers automatically adapt to the precise location of living tissues, organoids, or other biological materials rather than requiring perfect positioning beforehand.
Another experiment highlighted why precisely modeling light matters.
Previous demonstrations of volumetric overprinting treated metal components as though they completely absorbed incoming light. In reality, polished metal reflects and scatters significant amounts of light, creating distortions that reduce print quality.
Researchers tested both assumptions by printing a gear directly onto a polished steel rod. When the software ignored scattered light, the gear’s fine holes became over-polymerized and poorly defined.
After including realistic scattering behavior into the optical model, however, the resulting gear preserved those elaborate features far more accurately, increasing simulated print fidelity substantially. The improvement was confirmed experimentally.
Researchers even went as far as demonstrating that the process could print directly onto an electronic component.
Using an LED-based volumetric printing system, the researchers fabricated both a miniature lens and a small cross-shaped symbol directly onto the surface of a red LED. When illuminated, the printed lens projected the symbol onto a screen, successfully transforming the LED into a tiny optical projector without requiring additional assembly. The experiment showcased how optical elements themselves could potentially be integrated directly onto existing electronic hardware during manufacturing.
Beyond the individual demonstrations, the work additionally underscores an important trend in additive manufacturing: increasingly sophisticated software is becoming just as important as advances in hardware.
Instead of relying on simplified mathematical approximations, the new framework performs physically based optical simulations capable of modeling arbitrary geometries along with material properties.
The software has been released as open source, along with configuration files and experimental data, allowing other researchers to reproduce and expand upon the 3D printing work.
Although the current demonstrations remain laboratory-scale, the possible implications could extend well beyond experimental 3D printing. Biomedical researchers could eventually fabricate sterile microfluidic systems without multi-step assembly. Optical engineers may integrate lenses directly onto light sources. Manufacturers could add custom features to finished components without redesigning entire production processes.
Researchers also note that future improvements could further increase realism through incorporating additional physical effects, including changes in refractive index during polymerization and the diffusion of chemical inhibitors during printing. Those refinements could expand the technology’s accuracy and enable even more complex manufacturing scenarios.
“In summary, Dr.TVAM, our computational framework, has shown to be capable of modeling different optical situations for TVAM,” researchers write. “We believe this significantly expands the application space for TVAM, enabling the creation of complex, functionalized, and multi-component devices from applications in mechanics, optics, or biofabrication.”
Study: Wechsler F, et al. “Overprinting with tomographic volumetric additive manufacturing.” Nature Communications (2026).
Tim McMillan is a retired law enforcement executive, investigative reporter and co-founder of The Debrief. His writing typically focuses on defense, national security, the Intelligence Community and topics related to psychology. You can follow Tim on Twitter: @LtTimMcMillan. Tim can be reached by email: tim@thedebrief.org or through encrypted email: LtTimMcMillan@protonmail.com