Pattern-Based Analytics To Estimate And Track Yield Risk Of Designs Down To 7nm

Autonomous vehicles will cause fundamental shifts across a number of established industry segments tied to automotive, opening up big opportunities for chips and tools.

Better tools, more compute power, and more efficient algorithms are pushing this technology into the mainstream.

Goal is to improve quality while reducing time to revenue, but it’s not always so clear-cut.

But working with this architecture has some not-so-obvious pitfalls, and new tools licensing options may be necessary.

So far there are no tools and no clear methodology to eliminating bugs. That would require understanding what an AI bug actually is.

Part 1 of 2: How do you trade off cost and safety within an automobile? Plus, a look at some of the challenges the chip industry is facing.

The issues may be familiar, but they’re more difficult to solve and can affect everything from performance to yield.

When does a large amount of data become Big Data, and could system-level verification benefit from it?