Sunday, November 2, 2025

iPhones / Androids -- Hardware / Operating System -- Another Look

IA prompt:

iPhones / Androids. iPhone is a "brand" or a "make." Android is an operating system. Among Apple products there are multiple operating systems. Are there multiple "android operating systems" based on the hardware (desktop, laptop, table, phone, etc)? I assume Google Pixel uses a particular operating system. Do non-Apple / non-Chrome computers use an "android" operating system. Is there yet a third operating system, "Chrome operating system"? How do you sort this issue of operating systems  across different hardware devices?

Summary

Please generate that "Operating System Ecosystem Map -- 2025."

Saturday, November 1, 2025

Substrate Promises 2NM-Class Chipmaking At A Tenth Of The Cost Of EUV -- Tom's Hardware -- November 1, 2025

Link here.

However, the tool that Substrate is developing does not appear to be compatible with existing equipment and production flows, so the company will have to reinvent the whole supply chain to be successful. However, Substrate does not plan to sell its tool, but to build its own fab and provide foundry services. 

As integrated circuit features are getting smaller, chipmakers are using increasingly intricate lithography tools that now cost around $235 million for an ASML NXE:3800E Low-NA EUV scanner or around $380 million for an ASML EXE:5200B High-NA EUV scanner. As a result, fabs are becoming increasingly expensive to build, and chips are becoming more expensive to produce.

Substrate models that a leading-edge fab will cost around $50 billion by 2030, leaving semiconductor production to a handful of companies with very deep pockets. Furthermore, such fab expenditures are expected to increase the cost of a 300-mm wafer, which it claims could balloon to $100,000 when using leading-edge fabrication processes. This will make the development and production of advanced chips prohibitively expensive for small companies. Substrate intends to change that and reduce wafer pricing to just $10,000 by 2030. 

"At Substrate, we have a pathway to reduce the cost of leading-edge silicon by an order of magnitude compared to the current cost-scaling path we are on," a statement by the company reads. "By the end of the decade, Substrate will produce wafers closer to $10,000, not $100,000."

Note that Substrate is by no means the only company exploring particle accelerators as light sources for EUV or beyond-EUV lithography. In the U.S. alone, two companies — Inversion Semiconductor and xLight — as well as researchers at Johns Hopkins University, have revealed that they are working on lithography systems harnessing particle accelerators over the past 12 months. Chinese scientists and Japanese researchers are also testing particle accelerators for semiconductor production.

Substrate's x-ray lithography.

Substrate is developing a new type of lithography system that uses a particle accelerator to produce short-wavelength X-ray radiation (or light) for chipmaking. The goal is to replace ASML's expensive EUV lithography scanners with compact, low-cost machines capable of printing transistor patterns at a 2nm-class process technology (or even more advanced, the company claims). The firm claims the machine should reduce chip production costs by 10 times by the end of the decade.

At the core of Substrate’s technology is a custom particle accelerator which propels electrons (produced by an unknown emitter) to near the speed of light using radio-frequency cavities. As these electrons pass through sporadic magnetic fields, they gain kinetic energy, accelerating to speeds very close to the speed of light (a relativistic speed), which allows them to produce special types of light when manipulated. These fast-moving electrons fly through a series of  magnets that flip back and forth, wiggling the electrons and causing them to release their energy and produce coherent bursts of intense x-ray light (or radiation).

That light is 'billions of times brighter than the sun,' likely producing pulses intense enough to achieve the desired resolution and dose. The X-ray pulses are then focused by 'a succession of perfectly polished optics' to project a photomask onto a photoresist-coated silicon wafer. Note that Substrate never mentions reticle and resist in its official description, only claiming that 'bright pulses of light' are collimated and transported 'all the way to the silicon wafer,' which implies maskless direct-write lithography, which is good enough for research purposes, but orders of magnitude too slow for the mass production of chips. However, this remains speculation on our part.

In fact, Substrate's description of its technologies is very brief and lacks detail (perhaps for competitive reasons), making it difficult to analyze. However, since the company mentions X-ray, we are dealing with electromagnetic radiation with wavelengths ranging from 0.01nm to 10nm and energies from about 100 eV to 100 keV. The shorter the wavelength, the finer the structure that can the printed with improved accuracy, but the harder the light is to manage and work with. 

Given Substrate's achievements so far, we are likely dealing with soft X-rays (wavelengths of 1-10 nm, lower energy) rather than hard X-rays (wavelengths of 0.1-1nm, higher energy). 

Since short-wavelength light (including EUV and X-rays) is strongly absorbed by most materials, managing it requires a set of perfectly polished mirrors that reflect light at grazing angles (to avoid absorption), ultra-precise alignment, and vacuum environments. Also, X-ray lithography requires all-new resists that can handle high-energy photons without damage or blur. 

2nm-like CD and T2P spacing

To prove that its XRL method works, Substrate has shown off images of a random logic contact array with 12nm critical dimensions (CD) and 13nm tip-to-tip (T2T) spacing printed with high pattern fidelity, as well as random vias with a 30nm center-to-center pitch, possessing superb pattern quality and critical dimension uniformity. If such metrics could be achieved for mass production today, this would largely revolutionize the lithography industry, as it would enable scaling across both axis at 2nm-class nodes (and lower) without using multi-patterning.

Modern EUV scanners with 0.33 NA optics can achieve critical dimensions of 13nm–16nm in high-volume manufacturing, which is sufficient to print a 26nm minimum metal pitch (good enough for 2nm or 3nm-class process technologies) and a 25nm T2T interconnect space with a single exposure.

Such disproportions emerge because chipmakers tend to optimize resolution in the Y direction (CD) to get the tightest metal-pitch line-space pattern, but at the cost of resolution in the X direction, which means that T2T prints poorly or inconsistently, leading to bridging defects, stochastic defects, yield loss, complicated design rules, and slower scaling. To mitigate this and avoid blurred or inconsistent line ends at tip-to-tip spacing, Intel applies pattern-shaping tools in the X-direction with its 18A fabrication technology, but this complicates the overall production flow and does not fundamentally solve the issue.

Substrate's tool (assuming these are real lab results, not a simulation) can already outperform existing Low-NA EUV scanners in terms of achievable CDs with single-resolution patterning, and it leaves them behind dramatically when it comes to T2T spacing printed with high fidelity. This means that Substrate's X-ray lithography tool could possibly replace costly EUV multi-patterning used for sophisticated 3nm and 2nm-class process technologies or pattern shaping used for Intel 18A. 

Our friends at SemiAnalysis have managed to get more performance claims from Substrate, which look even more impressive. The company claims it has achieved overlay accuracy of under 1.6nm, full wafer critical-dimension uniformity (CDU) of 0.25 nm, line edge roughness (LER) of under 1nm, and local critical dimension uniformity (LCDU) below 1.5 nm.

If accurate, this performance would match or surpass ASML's Twinscan NXE:3800E in uniformity, though its overlay precision is slightly worse than the 0.9nm machine-matched overlay standard in the latest EUV scanners. Also, the line-width uniformity of contacts on an image provided by Substrate is rather poor.

Assuming the results presented by Substrate are real and achieved in a lab environment, this means the company has solved three critical challenges with X-ray lithography. First, build a light source featuring an electron gun and a particle accelerator; second, create a grazing-incidence mirror system to reflect and focus X-rays at very shallow angles; and make the whole thing compact enough to fit into a lab.

However, Substrate still has a lot of work to do, turning its X-ray lithography technology from a lab success into a viable production tool. Substrate must prove that its X-ray lithography system can maintain beam stability, optical precision, resist compatibility, overlay accuracy, and commercial throughput simultaneously, something no X-ray platform has ever achieved. 

Existing photoresists are incompatible with X-ray radiation, as they are optimized for EUV radiation with considerably lower photon energy. So, Substrate will have to invent a proper resist and then produce it at volume. The company will also have to develop photomasks that can sustain X-ray radiation. Grazing-incidence mirrors for X-rays are also not in mass production, and it is unknown whether they can be mass-produced cheaply and reliably by existing producers like Zeiss. 

Substrate will also have to ensure that X-rays do not damage the underlying transistors or introduce stochastic defects. Achieving overlay accuracy below 1nm (to match ASML's production-level alignment precision) remains another challenge for the company. This is perhaps because the company still has to address issues such as wafer handling, stage repeatability, and other factors related to high-precision mechanics, which ASML has taken decades to solve.

Beyond that, the tool must reach commercial throughput and consistent yield, something that took years for ASML's EUV tools. In fact, ASML's EUV journey timeline is quite exemplary: it has taken the industry 12 years to evolve from an alpha demo tool (2006) to mass production (2018), and about seven years to go from the first pre-production system (2010) to a mass-production-capable scanner.

Speaking of mass-production-capable X-ray lithography tools, it is important to note that Substrate has no intention of selling them to third parties such as Intel or TSMC. Instead, Substrate plans to build its own fabs in the U.S. (a move that could give the company geopolitical importance in the eyes of the U.S. government), install additional tools, and offer foundry services, thus challenging existing chip contract manufacturers. 

However, this strategy adds complexity and cost. Constructing even a single high-end semiconductor fabrication plant would require tens of billions of dollars in investment and a large ecosystem of suppliers and service infrastructure, which currently does not exist for X-ray lithography production.

Substrate would also need to integrate its XRL litho machines with hundreds of other tools in the fab, or persuade its suppliers (such as Applied Materials, KLA, Lam Research, etc.) to help it do so, which likely involves further investments from the company, making its first fab particularly expensive.

Also, running both a toolmaking activity and a chip foundry would stretch Substrate's technical and financial resources, which would make it particularly hard to achieve its promised per-wafer price of $10,000 by the end of the decade, as its investors will likely demand returns after pouring tens of billions of dollars into the company.

However, if Substrate succeeds in both roles, it could shift the balance of the semiconductor supply chain back to the U.S., as the company will likely outpace ASML's tools in terms of resolution and performance, and TSMC in terms of design cycle time and potentially volume.

Chatbot Timelines -- November 1, 2025

When -- what year -- was the average American able to download a chatbot; timeline of chatbots?



Computers: "In The Beginning," How Many GUIs Were There?

A review:

So, rather than a single number, the "beginning" involved a competitive landscape with several companies developing and releasing their own distinct graphical interfaces. 

Look at the time frame

  • baby footsteps: 1973
  • ten years later, 1883: the first commercial personal computer - Apple Lisa GUI
  • another two years, 1985: Microsoft Windows 1.0

 

Tuesday, October 28, 2025

LDCs: Update -- Hyperscalers -- October 28, 2025

AI prompt:

There are so many large data centers; impossible to keep up. Who are the biggest names today that would be in the same "universe" as Stargate; Horizon in the Permian; Utah Data Center; Meta in Prineville, OR, GAIIP; etc.

Reply:

Good question — and you’re absolutely right that the data-center universe is enormous and scattered. To help orient you, here are several of the biggest names / operators today that would be in the same league as the ultra‐large facilities you mentioned (e.g., Oracle’s “Stargate”, Meta Platforms’ Prineville, etc.). These firms either build, own or operate “hyperscale” data centers (large campuses, high power, global footprint).

Amazon Web Services (AWS)

  • AWS is widely acknowledged as the largest cloud/hyperscale operator. 

  • According to forecasts, AWS alone is expected to have 185 hyperscale data-center sites by 2030. 

  • These sites include massive campuses with multi-hundred-megawatt power draws and large land footprints.

  • So AWS clearly qualifies as one of the “big league” names you’re asking about.

Microsoft Coproation (Azure+ Cloud)

  • Microsoft is another of the top hyperscalers. Research shows Microsoft will have ~168 hyperscale sites by 2030. 

  • The scale and global reach of its data-centers put it clearly in that “universe” of very large players.

Google LLC (Google Cloud / GCP)

  • Google is similarly in the top tier. Research shows the three of AWS/Microsoft/Google account for ~60 % of all hyperscale data-center capacity. 

  • Their infrastructure, especially for AI, is at the very high end in both scale and sophistication.

Meta Platforms

  • Meta is also listed in major research as among the largest hyperscale operators. 

  • Its Prineville facility (which you mentioned) is a good example of a campus-scale deployment by one of these large operators.


Digital Realty Trust

  • Digital Realty is a major global data-center owner/operator (a REIT) with 300+ facilities in 25+ countries.While sometimes categorized as “colocation/wholesale” rather than exclusively cloud-owner, the scale is very large and global.

Equinix, Inc. 

  • Equinix operates 260 data centers in 33 countries (as of 2025).
  • While their model is more “carrier/colocation” oriented, they are still major players in the global data-center infrastructure landscape. 

Why These Matter 

  • These are the companies whose data-center infrastructure scale is in the same ballpark as the examples you cited: large land/energy footprints, multiple gigawatts of power, global campus builds.

  • If you think of the “universe” of major data-centers (hyperscale, cloud, AI-centric), these names sit at the core. Many smaller or regional players follow them.

  • Research shows that the total number of hyperscale data centers globally is large (~1,100+ by end of 2024) but the top handful (AWS, Microsoft, Google) dominate capacity. 

 *************************************

A table of the top 10 or 12 large data center operators:


LDC: GAIIP -- Allied Data Centers And CyrusOne -- October 28, 2025

 I track large data centers here. Somehow I missed this one: GAIIP.

Some speculators / influencers suggest Warren Buffett has invested in GAIIP. No evidence.



Has GAIIP invested in any large data centers yet?


Allied Data Centers:

Cyrus One: wiki.

Nvidia Announces Seven "New" Supercomputer Systems -- October 28, 2025

AI prompt

Jensen Huang / Nvidia today announced seven "new" supercomputers today at tech conference. Does this include previously announced "new" superconductors or are these truly seven new supercomputers?

Reply:

Good question — yes, NVIDIA (and Jensen Huang) did announce seven “new systems” (supercomputers) today. 


Here are the clarifications I can provide about what “new” means in this context:


✅ What we 

know

  • The press release from NVIDIA notes it is “accelerating seven new systems … across Argonne National Laboratory and Los Alamos National Laboratory (LANL).” 

  • At least two of the systems are singled out by name:

    • Solstice: described as “the U.S. Department of Energy’s largest AI supercomputer … will feature a record-breaking 100,000 NVIDIA Blackwell GPUs.” 

    • Equinox: another system with “10,000 NVIDIA Blackwell GPUs” expected in 2026. 

  • At LANL, two systems (Mission and Vision) will be built using the “Vera Rubin platform” and NVIDIA’s Quantum-X800 InfiniBand networking. 

  • The announcement is tied directly into the US national labs and the U.S. Department of Energy’s (DOE) AI-infrastructure mission.

 


⚠️ What 

we don’t

 know / what is ambiguous

  • It’s not entirely clear whether all seven systems are entirely new in the sense of “never-before-announced hardware builds,” or whether some are extensions/upgrades of previously announced projects.

  • Some systems like Solstice and Equinox may have been mentioned or planned previously, but today’s announcement seems to formalize deployment plans, partnerships, or scales.

  • The wording “accelerating seven new systems” might imply that these are new deployments (rather than incremental upgrades) but with embedded hardware (e.g., the Blackwell GPUs) that were in previous roadmaps.

  • There is a difference between “new supercomputer systems” vs “new GPUs/superchips” — this announcement is about the systems (supercomputers) built using those newer GPU generations.


🎯 My take


Based on the info, I believe the following is the most accurate summary:


NVIDIA is announcing seven new supercomputer systems (i.e., full installations of compute + networking + infrastructure) tied to U.S. national labs and major DOE projects. These are new launches of those systems (or at least new deployment announcements) rather than simply new GPU chips. The announcement uses previously-announced GPU architectures (like Blackwell, Vera Rubin) as building blocks, but the systems themselves are the fresh items.


So yes — you heard correctly that “seven new supercomputers” were announced — not just new chips or new “superconductors” (which perhaps was a mis-hearing) — the emphasis is on infrastructure (systems) rather than only semiconductor architecture.

 

The seven systems: