Author's Latest Posts


When Is Verification Complete?


Deciding when verification is done is becoming a much more difficult decision, prompting verification teams to increasingly rely on metrics rather than just the tests listed in the verification plan. This trend has been underway for the past couple of process nodes, but it takes time to spot trends and determine whether they are real or just aberrations. The Wilson Research Group conducts a ... » read more

System Bits: Aug. 22


Bioimaging technique tracks multiple in vivo interactions To make it possible to quickly and economically monitor multiple molecular interactions in a large area of living tissue – such as an organ or a small animal — Rensselaer Polytechnic Institute researchers have created an approach to optical imaging that could have applications in medical diagnosis, guided surgery, or pre-clinical dr... » read more

How Reliable Are FinFETs?


Stringent safety requirements in the automotive and industrial sectors are forcing chipmakers to re-examine a number of factors that can impact reliability over the lifespan of a device. Many of these concerns are not new. Electrical overstress (EOS), electrostatic discharge (ESD) and [getkc id="160" kc_name="electromigration"] (EM) are well understood, and have been addressed by EDA tools f... » read more

System Bits: Aug. 15


Machine-learning system for smoother streaming To combat the frustration of video buffering or pixelation, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “Pensieve,” an artificial intelligence system that uses machine learning to pick different algorithms depending on network conditions thereby delivering a higher-quality streaming exp... » read more

Applying Machine Learning


Sundari Mitra, co-founder and CEO of NetSpeed Systems, sat down with Semiconductor Engineering to discuss machine learning, training algorithms, what customers are struggling with today, and how startups fare in an increasingly consolidated semiconductor industry. What follows are excerpts of that conversation. SE: Machine learning is booming. How will this change design? Mitra: This is a... » read more

Computer Vision Powers Startups, Bleeding Edge Processes


You can’t turn around these days without walking into a convolutional neural network…..oh wait, maybe not yet, but sometime in the not-too-distant future, we’ll be riding in vehicles controlled by them. While not a new concept, CNNs are finally making the big time, as evidenced by a significant upswell in startup activity, tracked by Chris Rowen, CEO of Cognite Ventures. According to h... » read more

Using CNNs To Speed Up Systems


Convolutional neural networks (CNNs) are becoming one of the key differentiators in system performance, reversing a decades-old trend that equated speed with processor clock frequencies, the number of transistors, and the instruction set architecture. Even with today's smartphones and PCs, it's difficult for users to differentiate between processors with 6, 8 or 16 cores. But as the amount o... » read more

System Bits: Aug. 8


Improving robot vision, virtual reality, self-driving cars In order to generate information-rich images and video frames that will enable robots to better navigate the world and understand certain aspects of their environment, such as object distance and surface texture, engineers at Stanford University and the University of California San Diego have developed a camera that generates 4D images... » read more

Advanced Packaging Moves To Cars


By Ann Steffora Mutschler and Ed Sperling As automotive OEMs come up to speed on electrification of vehicles, each at their own pace, they are starting to embrace novel packaging approaches as a way to differentiate themselves in an increasingly competitive market. Wirebond used to dominate this market, where most of the chips were relatively unsophisticated and product cycles were slow... » read more

Using Machine Learning In EDA


Machine learning is beginning to have an impact on the EDA tools business, cutting the cost of designs by allowing tools to suggest solutions to common problems that would take design teams weeks or even months to work through. This reduces the cost of designs. It also potentially expands the market for EDA tools, opening the door to even new design starts and more chips from more compan... » read more

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