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Fabs Drive Deeper Into Machine Learning


Advanced machine learning is beginning to make inroads into yield enhancement methodology as fabs and equipment makers seek to identify defectivity patterns in wafer images with greater accuracy and speed. Each month a wafer fabrication factory produces tens of millions of wafer-level images from inspection, metrology, and test. Engineers must analyze that data to improve yield and to reject... » read more

NN-Baton: DNN Workload Orchestration & Chiplet Granularity Exploration for Multichip Accelerators


"Abstract—The revolution of machine learning poses an unprecedented demand for computation resources, urging more transistors on a single monolithic chip, which is not sustainable in the Post-Moore era. The multichip integration with small functional dies, called chiplets, can reduce the manufacturing cost, improve the fabrication yield, and achieve die-level reuse for different system scales... » read more

Applications, Challenges For Using AI In Fabs


Experts at the Table: Semiconductor Engineering sat down to discuss chip scaling, transistors, new architectures, and packaging with Jerry Chen, head of global business development for manufacturing & industrials at Nvidia; David Fried, vice president of computational products at Lam Research; Mark Shirey, vice president of marketing and applications at KLA; and Aki Fujimura, CEO of D2S. Wh... » read more

Deep Learning (DL) Applications In Photomask To Wafer Semiconductor Manufacturing


The Survey: 2021 Deep Learning Applications List by eBeam Initiative members is a list of current deep learning efforts that are being used in photomask to wafer semiconductor manufacturing. Examples come from ASML, D2S, Fraunhofer IPMS, Hitachi High-Tech Corporation, imec, Siemens Industries Software, Inc., Siemens EDA, STMicroelectronics, and TASMIT. Published by the eBeam Initiative Membe... » read more

Hidden Costs In Faster, Low-Power AI Systems


Chipmakers are building orders of magnitude better performance and energy efficiency into smart devices, but to achieve those goals they also are making tradeoffs that will have far-reaching, long-lasting, and in some cases unknown impacts. Much of this activity is a direct result of pushing intelligence out to the edge, where it is needed to process, sort, and manage massive increases in da... » read more

AI And High-NA EUV At 3/2/1nm


Semiconductor Engineering sat down to discuss lithography and photomask issues with Bryan Kasprowicz, director of technology and strategy and a distinguished member of the technical staff at Photronics; Harry Levinson, principal at HJL Lithography; Noriaki Nakayamada, senior technologist at NuFlare; and Aki Fujimura, chief executive of D2S. What follows are excerpts of that conversation. To vie... » read more

What’s Next In AI, Chips And Masks


Aki Fujimura, chief executive of D2S, sat down with Semiconductor Engineering to talk about AI and Moore’s Law, lithography, and photomask technologies. What follows are excerpts of that conversation. SE: In the eBeam Initiative’s recent Luminary Survey, the participants had some interesting observations about the outlook for the photomask market. What were those observations? Fujimur... » read more

Deploying Accurate Always-On Face Unlock


Accurate face verification has long been considered a challenge due to the number of variables, ranging from lighting to pose and facial expression. This white paper looks at a new approach — combining classic and modern machine learning (deep learning) techniques — that achieves 98.36% accuracy, running efficiently on Arm ML-optimized platforms, and addressing key security issues such a... » read more

3 Types Of AI Hardware


As AI chips become more pervasive, three primary approaches are moving to the forefront. Bradley Geden, director of product marketing at Synopsys, looks at how to take advantage of repeatability, what the different flavors look like, the difference between flat and hierarchical design, and what impact black-box arrays have on programmability. » read more

It’s Eternal Spring For AI


The field of Artificial Intelligence (AI) has had many ups and downs largely due to unrealistic expectations created by everyone involved including researchers, sponsors, developers, and even consumers. The “reemergence” of AI has lot to do with recent developments in supporting technologies and fields such as sensors, computing at macro and micro scales, communication networks and progre... » read more

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