Measuring temperature with neutrons

Researchers from Osaka University, National Institutes for Quantum Science and Technology, Hokkaido University, Japan Atomic Energy Agency, and Tokamak Energy developed a way to rapidly measure the temperature of electronic components inside a device using neutrons.

The technique, called ‘neutron resonance absorption’ (NRA), examines neutrons being absorbed by atomic nuclei at certain energy levels to determine the properties of the material. After being generated using high-intensity laser beans, the neutrons were then decelerated to a very low energy level before being passed through the sample, in this case plates of tantalum and silver. The temporal signal of the NRA was altered in a predictable manner when the sample material’s temperature was changed.

“This technology makes it possible to instantaneously and accurately measure temperature,” said Zechen Lan of Osaka University, in a statement. “As our method is non-destructive, it can be used to monitor devices like batteries and semiconductor devices.”

The technique can acquire temperature data in a window of 100 nanoseconds, and the measurement device itself is about a tenth of the size of similar equipment.

“Using lasers to generate and accelerate ions and neutrons is nothing new, but the techniques we’ve developed in this study represent an exciting advance,” added Akifumi Yogo of Osaka University, in a statement. “We expect that the high temporal resolution will allow electronics to be examined in greater detail, help us to understand normal operating conditions, and pinpoint abnormalities.” [1]

Mapping heat transfer

Researchers from the University of Rochester applied optical super-resolution fluorescence microscopy techniques used in biological imaging to map heat transfer in electronic devices using luminescent nanoparticles.

By applying highly doped upconverting nanoparticles to the surface of a device, the researchers were able to achieve super-high resolution thermometry at the nanoscale level from up to 10 millimeters away.

Rochester researchers demonstrated their super-high resolution thermometry techniques on an electrical heater structure that the team designed to produce sharp temperature gradients. (Credit: University of Rochester / J. Adam Fenster)

“The building blocks of our modern electronics are transistors with nanoscale features, so to understand which parts of overheating, the first step is to get a detailed temperature map,” said Andrea Pickel, an assistant professor from the University of Rochester’s Department of Mechanical Engineering, in a release. “But you need something with nanoscale resolution to do that.”

The researchers demonstrated the technique using an electrical heater structure designed to produce sharp temperature gradients. To improve the process, the team hopes to lower the laser power used and refine the methods for applying layers of nanoparticles to the devices. [2]

ML for predicting thermal properties

Researchers from MIT, Argonne National Laboratory, Harvard University, the University of South Carolina, Emory University, the University of California at Santa Barbara, and Oak Ridge National Laboratory propose a new machine learning framework that provides much faster prediction of phonon dispersion relations, an important measurement for determining the thermal properties of a material and how heat moves through semiconductors and insulators.

Heat-carrying phonons have an extremely wide frequency range, and the particles interact and travel at different speeds. “Phonons are the culprit for the thermal loss, yet obtaining their properties is notoriously challenging, either computationally or experimentally,” said Mingda Li, associate professor of nuclear science and engineering at MIT, in a release.

The researchers started with a graph neural network (GNN) that converts a material’s atomic structure into a crystal graph comprising multiple nodes, which represent atoms, connected by edges, which represent the interatomic bonding between atoms.

To make it suitable for predicting phonon dispersion relations, they created a virtual node graph neural network (VGNN) by adding a series of flexible virtual nodes to the fixed crystal structure to represent phonons. This enabled the VGNN to skip many complex calculations when estimating phonon dispersion relations, making it a more efficient method than a standard GNN.

Li noted that a VGNN could be used to calculate phonon dispersion relations for a few thousand materials in a few seconds with a personal computer. The technique could also be used to predict challenging optical and magnetic properties. [3]

References

[1] Lan, Z., Arikawa, Y., Mirfayzi, S.R. et al. Single-shot laser-driven neutron resonance spectroscopy for temperature profiling. Nat Commun 15, 5365 (2024). https://doi.org/10.1038/s41467-024-49142-y

[2] Ziyang Ye et al., Optical super-resolution nanothermometry via stimulated emission depletion imaging of upconverting nanoparticles. Sci. Adv. 10, eado6268 (2024) https://doi.org/10.1126/sciadv.ado6268

[3] Okabe, R., Chotrattanapituk, A., Boonkird, A. et al. Virtual node graph neural network for full phonon prediction. Nat Comput Sci 4, 522–531 (2024). https://doi.org/10.1038/s43588-024-00661-0

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Rapidus and IBM are jointly developing mass production capabilities for chiplet-based advanced packages. The collaboration builds on an existing agreement to develop 2nm process technology.

Vanguard and NXP will jointly establish VisionPower Semiconductor Manufacturing Company (VSMC) in Singapore to build a $7.8 billion, 12-inch wafer plant. This is part of a global supply chain shift “Out of China, Out of Taiwan,” according to TrendForce.

Alphawave joined forces with Arm to develop an advanced chiplet based on Arm’s Neoverse Compute Subystems for AI/ML. The chiplet contains the Neoverse N3 CPU core cluster and Arm Coherent Mesh Network, and will be targeted at HPC in data centers, AI/ML applications, and 5G/6G infrastructure.

ElevATE Semiconductor and GlobalFoundries will partner for high-voltage chips to be produced at GF’s facility in Essex Junction, Vermont, which GF bought from IBM. The chips are essential for semiconductor testing equipment, aerospace, and defense systems.

NVIDIA, OpenAI, and Microsoft are under investigation by the U.S. Federal Trade Commission and Justice Department for violation of antitrust laws in the generative AI industry, according to the New York Times.

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Global

Apollo Global Management will invest $11 billion in Intel’s Fab 34 in Ireland, thereby acquiring a 49% stake in Intel’s Irish manufacturing operations.

imec and ASML opened their jointly run High-NA EUV Lithography Lab in Veldhoven, the Netherlands. The lab will be used to prepare  the next-generation litho for high-volume manufacturing, expected to begin in 2025 or 2026.

Expedera opened a new semiconductor IP design center in India. The location, the sixth of its kind for the company, is aimed at helping to make up for a shortfall in trained technicians, researchers, and engineers in the semiconductor sector.

Foxconn will build an advanced computing center in Taiwan with NVIDIA’s Blackwell platform at its core. The site will feature GB200 servers, which consist of 64 racks and 4,608 GPUs, and will be completed by 2026.

Intel and its 14 partner companies in Japan will use Sharp‘s LCD plants to research semiconductor production technology, a cost reduction move that should also produce income for Sharp, according to Nikkei Asia.

Japan is considering legislation to support the commercial production of advanced semiconductors, per Reuters.

Saudi Arabia aims to establish at least 50 semiconductor design companies as part of a new National Semiconductor Hub, funded with over $266 million.

Air Liquide is opening a new industrial gas production facility in Idaho, which will produce ultra-pure nitrogen and other gases for Micron’s new fab.

Microsoft will invest 33.7 billion Swedish crowns ($3.2 billion) to expand its cloud and AI infrastructure in Sweden over a two-year period, reports Bloomberg. The company also will invest $1 billion to establish a new data center in northwest Indiana.

AI data centers could consume as much as 9.1% of the electricity generated in the U.S. by 2030, according to a white paper published by the Electric Power Research Institute. That would more than double the electricity currently consumed by data centers, though EPRI notes this is a worst case scenario and advances in efficiency could be a mitigating factor.

Markets and Money

The Semiconductor Industry Association (SIA) announced global semiconductor sales increased 15.8% year-over-year in April, and the group projected a market growth of 16% in 2024. Conversely, global semiconductor equipment billings contracted 2% year-over-year to US$26.4 billion in Q1 2024, while quarter-over-quarter billings dropped 6% during the same period, according to SEMI‘s Worldwide Semiconductor Equipment Market Statistics (WWSEMS) Report.

Cadence completed its acquisition of BETA CAE Systems International, a provider of multi-domain, engineering simulation solutions.

Cisco‘s investment arm launched a $1 billion fund to aid AI startups as part of its AI innovation strategy. Nearly $200 million has already been earmarked.

The power and RF GaN markets will grow beyond US$2.45 billion and US$1.9 billion in 2029, respectively, according to Yole, which is offering a webinar on the topic.

The micro LED chip market is predicted to reach $580 million by 2028, driven by head-mounted devices and automotive applications, according to TrendForce. The cost of Micro LED chips may eventually come down due to size miniaturization.

In-Depth

Semiconductor Engineering published its Automotive, Security, and Pervasive Computing newsletter this week, featuring these top stories:

• The Uncertainty Of Certifying AI For Automotive
• Why It’s So Hard To Secure AI Chips
• Toward A Software-Defined Hardware World

More reporting this week:

• Chip Design Digs Deeper Into AI
• Why IC Design Safety Nets Have Limits
• Opportunities Grow For GPU Acceleration

Security

Scott Best, Rambus senior director of Silicon Security Products, delivered a keynote at the Hardwear.io conference this week (below), detailing a $60 billion reverse engineering threat for hardware in just three markets — $30 billion for printer consumables, $20 billion for rechargeable batteries with some type of authentication, and $10 billion for medical devices such as sonogram probes.

Photo source: Ed Sperling/Semiconductor Engineering

wolfSSL debuted wolfHSM for automotive hardware security modules, with its cryptographic library ported to run in automotive HSMs like Infineon’s Aurix Tricore TC3XX.

Cisco integrated AMD Pensando data processing units (DPUs) with its Hypershield security architecture for defending AI-scale data centers.

OMNIVISION released an intelligent CMOS image sensor for human presence detection, infrared facial authentication, and always-on technology with a single sensing camera. And two new image sensors for industrial and consumer security surveillance cameras.

Digital Catapult announced a new cohort of companies will join Digital Security by Design’s Technology Access Program, gaining access to an Arm Morello prototype evaluation hardware kit based on Capability Hardware Enhanced RISC Instructions (CHERI), to find applications across critical UK sectors.

University of Southampton researchers used formal verification to evaluate the hardware reliability of a RISC-V ibex core in the presence of soft errors.

Several institutions published their students’ master’s and PhD work:

• Virginia Tech published a dissertation proposing sPACtre, a defense mechanism that aims to prevent Spectre control-flow attacks on existing hardware.
• Wright State University published a thesis proposing an approach that uses various machine learning models to bring an improvement in hardware Trojan identification with power signal side channel analysis
• Wright State University published a thesis examining the effect of aging on the reliability of SRAM PUFs used for secure and trusted microelectronics IC applications.
• Nanyang Technological University published a Final Year Project proposing a novel SAT-based circuit preprocessing attack based on the concept of logic cones to enhance the efficacy of SAT attacks on complex circuits like multipliers.

The Cybersecurity and Infrastructure Security Agency (CISA) issued a number of alerts/advisories.

Education and Training

Renesas and the Indian Institute of Technology Hyderabad (IIT Hyderabad) signed a three-year MoU to collaborate on VLSI and embedded semiconductor systems, with a focus on R&D and academic interactions to advance the “Make in India” strategy.

Charlie Parker, senior machine learning engineer at Tignis, presented a talk on “Why Every Fab Should Be Using AI.”

Penn State and the National Sun Yat-Sen University (NSYSU) in Taiwan partnered to develop educational and research programs focused on semiconductors and photonics.

Rapidus and Hokkaido University partnered on education and research to enhance Japan’s scientific and technological capabilities and develop human resources for the semiconductor industry.

The University of Minnesota named Steve Koester its first “Chief Semiconductor Officer,” and launched a website devoted to semiconductor and microelectronics research and education.

The state of Michigan invested $10 million toward semiconductor workforce development.

Product News

Siemens reported breakthroughs in high-level C++ verification that will be used in conjunction with its Catapult software. Designers will be able to use formal property checking via the Catapult Formal Assert software and reachability coverage analysis through Catapult Formal CoverCheck.

Infineon released several products:

• 600 V CoolMOS 8 high voltage superjunction (SJ) MOSFET product family.
• CoolGaN bidirectional switch (BDS) and CoolGaN Smart Sense device.
• New CoolSiC MOSFET discretes 650 V, with two product families housed in the Thin-TOLL 8×8 and TOLT packages to increase system power density.

Augmental, an MIT Media Lab spinoff, released a tongue-based computer controller, dubbed the MouthPad.

NVIDIA revealed a new line of products that will form the basis of next-gen AI data centers. Along with partners ASRock Rack, ASUS, GIGABYTE, Ingrasys, and others, the NVIDIA GPUs and networking tech will offer cloud, on-premises, embedded, and edge AI systems. NVIDIA founder and CEO Jensen Huang showed off the company’s upcoming Rubin platform, which will succeed its current Blackwell platform. The new system will feature new GPUs, an Arm-based CPU and advanced networking with NVLink 6, CX9 SuperNIC and X1600 converged InfiniBand/Ethernet switch.

Intel showed off its Xeon 6 processors at Computex 2024. The company also unveiled architectural details for its Lunar Lake client computing processor, which will use 40% less SoC power, as well as a new NPU, and X2 graphic processing unit cores for gaming.

Research

imec released a roadmap for superconducting digital technology to revolutionize AI/ML.

CEA-Leti reported breakthroughs in three projects it considers key to the next generation of CMOS image sensors. The projects involved embedding AI in the CIS and stacking multiple dies to create 3D architectures.

Researchers from MIT’s Computer Science & Artificial Intelligence Laboratory (MIT-CSAIL) used a type of generative AI, known as diffusion models, to train multi-purpose robots, and designed the Grasping Neural Process for more intelligent robotic grasping.

IBM and Pasqal partnered to develop a common approach to quantum-centric supercomputing and to promote application research in chemistry and materials science.

Stanford University and Q-NEXT researchers investigated diamond to find the source of its temperamental nature when it comes to emitting quantum signals.

TU Wien researchers investigated how AI categorizes images.

In Canada:

• Simon Fraser University received funding of over $80 million from various sources to upgrade the supercomputing facility at the Cedar National Host Site.
• The Digital Research Alliance of Canada announced $10.28 million to renew the University of Victoria’s Arbutus cloud infrastructure.
• The Canadian government invested $18.4 million in quantum research at the University of Waterloo.

Events and Further Reading

Find upcoming chip industry events here, including:

Upcoming webinars are here.

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