Can ML Help Verification? Maybe


Functional verification produces an enormous amount of data that could be used to train a machine learning system, but it's not always clear which data is useful or whether it can help. The challenge with ML is understanding when and where to use it, and how to integrate it with other tools and approaches. With a big enough hammer, it is tempting to call everything a nail, and just throwing ... » read more

Progress In Quantum Computing


A recent wave of quantum computing investment has given rise to claims of a quantum computing bubble, based on overly optimistic technological claims in a field area that experts say has yet to demonstrate any real utility. But executives on the industry’s front lines say quantum computing is indeed a commercially viable technology, albeit one that is at least several years away from overcomi... » read more

How Memory Design Optimizes System Performance


Exponential increases in data and demand for improved performance to process that data has spawned a variety of new approaches to processor design and packaging, but it also is driving big changes on the memory side. While the underlying technology still looks very familiar, the real shift is in the way those memories are connected to processing elements and various components within a syste... » read more

10 Questions: Handel Jones


Handel Jones, CEO of International Business Strategies and author of a new book, "When AI Rules The World," sat down with Semiconductor Engineering to talk about the growth and impact of AI. What follows are excerpts of that conversation. SE: What do you see as the impact of AI on semiconductors? Jones: The fact that you have a 5G smart phone is because of AI. Steve Jobs changed the smart... » read more

Why Geofencing Will Enable L5


What will it take for a car to be able to drive itself anywhere a human can? Ask autonomous vehicle experts this question and the answer invariably includes a discussion of geofencing. In the broadest sense, geofencing is simply a virtual boundary around a physical area. In the world of self-driving cars, it describes a crucial subset of the operational design domain — the geographic regio... » read more

Improving Redistribution Layers for Fan-out Packages And SiPs


Redistribution layers (RDLs) are used throughout advanced packaging schemes today including fan-out packages, fan-out chip on substrate approaches, fan-out package-on-package, silicon photonics, and 2.5D/3D integrated approaches. The industry is embracing a variety of fan-out packages especially because they deliver design flexibility, very small footprint, and cost-effective electrical connect... » read more

How To Compare Chips


Traditional metrics for semiconductors are becoming much less meaningful in the most advanced designs. The number of transistors packed into a square centimeter only matters if they can be utilized, and performance per watt is irrelevant if sufficient power cannot be delivered to all of the transistors. The consensus across the chip industry is that the cost per transistor is rising at each ... » read more

The High Price Of Smaller Features


The semiconductor industry’s push for higher numerical apertures is driven by the relationship between NA and critical dimension. As the NA goes up, the CD goes down: Where λ is the wavelength and k1 is a process coefficient. While 0.55 NA exposure systems will improve resolution, Larry Melvin, principal engineer at Synopsys, noted that smaller features always come with a process cos... » read more

How Mature Are Verification Methodologies?


Semiconductor Engineering sat down to discuss differences between hardware and software verification and changes and challenges facing the chip industry, with Larry Lapides, vice president of sales for Imperas Software; Mike Thompson, director of engineering for the verification task group at OpenHW; Paul Graykowski, technical marketing manager for Arteris IP; Shantanu Ganguly, vice president o... » read more

Rethinking Machine Learning For Power


The power consumed by machine learning is exploding, and while advances are being made in reducing the power consumed by them, model sizes and training sets are increasing even faster. Even with the introduction of fabrication technology advances, specialized architectures, and the application of optimization techniques, the trend is disturbing. Couple that with the explosion in edge devices... » read more

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