AI’s Power To Transform Semiconductor Design And Manufacturing


Artificial intelligence and machine learning (AI/ML) have immense power to transform semiconductor design and manufacturing for a variety of broad and far-ranging applications. Just consider the volume of data generated by design and manufacturing each year. With increasingly complex products, machines, processes and supply chains, the overall amount of data associated with semiconductor making... » read more

Chip Industry Week In Review


Europe's top court ruled in Intel's favor, voiding a $1.1 billion fine imposed by the European Union and dismissing charges of anti-competitive behavior. IBM released yield benchmarks for high-NA EUV, which serve as proof points that the newest advanced litho equipment will enable scaling beyond the 2nm process node. Also on the lithography front, Nikon is developing a maskless digital litho... » read more

Reactionary Or Anticipatory?


The EDA industry is located at an interesting place, where anticipation and reaction come together. Too much of either one is wasteful, but too little leaves the industry having to deal with unwanted problems. We see this happening in several areas today, and the balance is changing for several reasons. We normally expect universities to be 100% anticipatory. There is no point in them worki... » read more

LLMs Show Promise In Secure IC Design


The introduction of large language models into the EDA flow could significantly reduce the time, effort, and cost of designing secure chips and systems, but they also could open the door to more sophisticated attacks. It's still early days for the use of LLMs in chip and system design. The technology is just beginning to be implemented, and there are numerous technical challenges that must b... » read more

In Memory, At Memory, Near Memory: What Would Goldilocks Choose?


The children’s fairy tale of ‘Goldilocks and the Three Bears’ describes the adventures of Goldi as she tries to choose among three choices for bedding, chairs, and bowls of porridge. One meal is “too hot,” the other “too cold,” and finally one is “just right.” If Goldi were faced with making architecture choices for AI processing in modern edge/device SoCs, she would also face... » read more

GDDR7 Memory Supercharges AI Inference


GDDR7 is the state-of-the-art graphics memory solution with a performance roadmap of up to 48 Gigatransfers per second (GT/s) and memory throughput of 192 GB/s per GDDR7 memory device. The next generation of GPUs and accelerators for AI inference will use GDDR7 memory to provide the memory bandwidth needed for these demanding workloads. AI is two applications: training and inference. With tr... » read more

Real-Time Low Light Video Enhancement Using Neural Networks On Mobile


Video conferencing is a ubiquitous tool for communication, especially for remote work and social interactions. However, it is not always a straightforward plug and play experience, as adjustments may be needed to ensure a good audio and video setup. Lighting is one such factor that can be tricky to get right. A nicely illuminated video feed looks presentable in a meeting, but on the other hand,... » read more

Mass Customization For AI Inference


Rising complexity in AI models and an explosion in the number and variety of networks is leaving chipmakers torn between fixed-function acceleration and more programmable accelerators, and creating some novel approaches that include some of both. By all accounts, a general-purpose approach to AI processing is not meeting the grade. General-purpose processors are exactly that. They're not des... » read more

Big Changes In Optical Inspection


Optical inspection always has been the workhorse technology for finding defects in chips. It's fast, cost-efficient, and generally reliable enough for most chips. But as logic scales into the angstrom range, and as systems become collections of chiplets, optical inspection needs to be combined with other types of inspection such as X-ray and acoustic. Kyle Vander Schaaf, application engineer at... » read more

ML Model Usage For Various Life Stages Of Semiconductor Test


By Shinji Hioki and Ken Butler From development through high volume manufacturing (HVM), semiconductor manufacturers’ pain points change based on the life stages. Each stage requires different types of applications to help with business needs. At the early stage, where the design and process are still immature, understanding the root causes of maverick material and implementing fixes is th... » read more

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