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US government, tech giants and Biohub commit $1.8B to AI biology initiative

Credited to SiliconANGLE · siliconangle.com21h ago

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More than a half-dozen public and private sector organizations are teaming up to develop biology datasets for artificial intelligence models.

The consortium, which announced the collaboration today, is committing $1.8 billion to the project.

Studying how a cell responds to a new therapy usually requires scientists to set up a physical test environment. That can take years of work and millions of dollars. A faster, more cost-efficient approach would be to test therapies using simulated cells. A simulation doesn’t require expensive lab equipment and can theoretically be spun up in a few hours.

Ultra-detailed, highly reliable simulations that can substitute lab equipment aren’t yet available. The reason is that creating such virtual cells would require a new generation of biology-optimized AI models. Training such models would only be possible with bigger biology datasets than those presently available to scientists.

The research initiative announced today aims to produce the necessary datasets. The biggest contributor to the project is the U.S. Department of Energy, which will commit more than $500 million over five years.

Some of the funds are earmarked for microscopy equipment. The hardware will be used to collect cell measurements for AI training datasets.

The Energy Department will prioritize three imaging methods: neutron scattering, X-ray and cryo-electron microscopy. The first two methods identify the materials that make up a biological sample. Neutron scattering equipment spots light elements and their isotopes, while X-ray machines detect heavier elements. Cryo-electron microscopy, the third imaging method on the list, maps out the internal structure of cells.

The second biggest contributor to the project is Biohub, a research nonprofit backed by Mark Zuckerberg. It’s committing $400 million to the initiative. Three quarters of the funds will go toward in-house research endeavors, while the rest will support external projects.

Original · SiliconANGLE

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More than a half-dozen public and private sector organizations are teaming up to develop biology datasets for artificial intelligence models. The consortium, which announced the collaboration today, is committing $1.8 billion to the project. Studying how a cell responds to a new…

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Models & Labs · United States

Aehr Expands AI Chip Burn-In Capacity with $6M Hyperscaler Order

Credited to Semiconductor Digest · semiconductor-digest.com · 23h ago

Aehr Test Systems, a provider of test and burn-in solutions for semiconductor devices used in artificial intelligence (AI), silicon photonics, data center, automotive, and industrial applications, today announced approximately $6 million in follow-on production orders from its lead hyperscale customer for package-level burn-in (PLBI) of its next-generation AI processor for data center training and inference applications.

The orders include additional Sonoma™ high-power package-level test and burn-in systems, fully turnkey burn-in modules (BIMs) and device-specific sockets, as well as production software enhancements. The systems and consumables will be deployed at one of the customer’s outsourced semiconductor assembly and test (OSAT) partners in Taiwan. Deliveries under the order are scheduled over the next six months.

This next-generation AI processor is the second from this customer to use Aehr’s Sonoma platform for production burn-in. Following the previously announced initial production win, these orders expand the customer’s capacity ahead of an expected production ramp of this second AI device in calendar 2027. The customer has indicated that this second AI processor is expected to ultimately reach production volumes exceeding those of the customer’s first processor brought into production burn-in on the Sonoma platform. That first processor is already in high-volume production, with volumes expected to continue ramping significantly as additional Sonoma system capacity is deployed over the next several months.

“These follow-on production orders demonstrate the continued expansion of our relationship with this hyperscale customer and its confidence in Sonoma to support successive generations of increasingly powerful and complex AI processors,” said Gayn Erickson, President and Chief Executive Officer of Aehr Test Systems. “The program is progressing from qualification and initial production systems to additional systems, consumables and software supporting the anticipated production ramp. As volumes increase, we believe there is potential for significant additional demand for Sonoma systems and device-specific consumables.”

The next-generation processor requires independently controlled thermal zones within each package to achieve the operating conditions needed for effective production burn-in and early-life failure screening. Sonoma’s multi-zone thermal control capabilities enable independent temperature management across different areas of a device while delivering the high current required to electrically stress the processor.

The platform also supports independent electrical test vectors and monitoring of multiple devices or functional elements within a multi-chip package. These capabilities address the differing power densities and thermal characteristics of advanced AI packages, which increasingly combine compute die, high-bandwidth memory and interface components.

“As AI processors become more complex, controlling the temperature of the package as a whole is no longer sufficient for some applications,” Erickson continued. “Sonoma’s ability to independently manage multiple thermal zones and electrical test functions helps customers address these challenges. We believe these capabilities will become increasingly important as AI packages continue to grow in power, size, complexity and value.”

Aehr is also engaged in detailed discussions with this customer regarding reliability qualification and production burn-in requirements for future generations of higher-power, larger AI devices. These discussions include both Aehr’s Sonoma package-level and FOX-XP™ wafer -level burn-in (WLBI) platforms.

“As the value of advanced AI packages increases, identifying early-life failures before expensive packaging can deliver significant economic benefits,” Erickson added. “WLBI can screen die before assembly, while PLBI can stress and screen the completed device. Customers may choose either approach or use both at different stages of qualification and production, depending on their requirements.”

“Our ability to support both approaches positions Aehr well as customers develop reliability strategies for future AI processors. With Sonoma supporting this customer’s first processor in high-volume production and now its next-generation program, we are building a foundation for continued growth as the customer’s AI roadmap advances.”