tag: machine learning

AI Models Transform Defect Inspection And Review, But Can Fail To Scale
By Laura Peters - 09 Jun, 2026 - Comments: 0

Key Takeaways: AI plays a role in improving defect capture rate and distinguishing between yield-killing and nuisance defects. New developments in wafer edge inspection are proving essential to bonded wafer yields. 70% of AI initiatives stall after pilot implementation, but some pitfalls can be avoided. One of the brightest spots in AI use today is the industry’s ability t... » read more

Improving GPU Energy Efficiency With Component-Level Power Management (AMD)
By Technical Paper Link - 25 May, 2026 - Comments: 0

Researchers from AMD released “CompPow: A Case for Component-level GPU Power Management”. Abstract “The ever increasing demand for ML-driven intelligence in a wide spectrum of domains has led to ubiquity of GPUs. At the same time, GPUs are notorious for their power consumption needs and often dominate power allocation in a typical ML datacenter. While datacenter-level power opti... » read more

Beyond Ideal Crystals: The Case For Scale In Atomistic Modeling
By Anders Blom - 21 May, 2026 - Comments: 0

Almost all computer simulations face the same trade-off: larger models can be more realistic and therefore more useful, but they also take longer to run. Engineers and scientists are therefore faced with an almost daily challenge of choosing a model that is detailed enough to capture the important details without making the calculation impractically expensive. "All models are wrong, but some... » read more

Evaluating and Calibrating Performance On RISC-V Vector Processors (KTH, LLNL, BSC)
By Technical Paper Link - 18 May, 2026 - Comments: 0

A new technical paper, "Closer in the Gap: Towards Portable Performance on RISC-V Vector Processors," was published by researchers at KTH Royal Institute of Technology, Lawrence Livermore National Laboratory, and Barcelona Supercomputing Center. Abstract "The RISC-V Vector Extension~(RVV) is a cornerstone for supporting compute throughout in scientific and machine learning workloads. Yet ... » read more

What’s Really Needed For Advanced Test?
By Greg Prewitt - 12 May, 2026 - Comments: 0

By Greg Prewitt and Marc Jacobs Advanced test has become one of the semiconductor industry's most promising frontiers: adaptive binning, feed-forward models, and real-time analytics pulling signals from mountains of measurement data. But there is a problem hiding underneath all that ambition, and it is neither compute nor algorithm; it is data. More specifically, it is the unglamorous, found... » read more

Smart Test Collides With The Data Chain
By Gregory Haley - 12 May, 2026 - Comments: 0

Key Takeaways: The promise of smart test is a data-chain problem before it is an algorithm problem. A device can pass every checkpoint and still carry a latent defect the test record never captured. As test grows more adaptive, the validity of the measurement environment matters as much as the measurement itself. For years, the test roadmap has pointed toward more adaptive f... » read more

When Semiconductor Materials Misbehave
By Gregory Haley - 27 Apr, 2026 - Comments: 0

Key Takeaways Material behavior in production depends on the process context that no development environment can fully replicate. In advanced packaging, the interactions that cross domain boundaries are increasingly where failures originate. The most accurate materials data is also the most commercially sensitive, leaving simulation models calibrated against generic inputs rather tha... » read more

System-in-Package Challenges
By Ed Sperling - 23 Apr, 2026 - Comments: 0

Systems companies and leading-edge chipmakers are pushing past reticle limits with chiplet-based designs, often breaking compute-intensive functions into different chiplets and coupling those with other chiplets that may have been developed by different teams and at different process nodes. This is harder than it sounds, and results can vary widely even under the best circumstances. Nir Sever, ... » read more

The Smart Advantage: How Artificial Intelligence Is Transforming Inspection And Metrology In Semiconductor Manufacturing
By Charlie Zhu - 14 Apr, 2026 - Comments: 0

There is no doubt that the semiconductor industry is in an era of rapid and profound transformation, driven by an increasing demand for smaller, faster, and more powerful chips. As the speed of innovation continues to advance, so does the pressure on semiconductor manufacturers to detect and address defects and inconsistencies with near-perfect accuracy to keep pace with this demand. Manual ... » read more

Force Fields Will Accelerate Atomistic Simulations By 10,000× In 2026, Unlocking New Era Of Discovery
By Anders Blom - 19 Feb, 2026 - Comments: 0

By Anders Blom and Igor Markov “Force fields” have long captured our imagination — the invisible shields of science-fiction lore that protect starships and superheroes from harm. But in the world of scientific discovery, force fields play a much different role: They are mathematical models that let us peer into the atomic heart of matter itself. Now, thanks to breakthroughs in artif... » read more

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