Cut Defects, Not Yield


Many chipmakers face a difficult trade-off — improve quality without affecting yield. Traditional testing methods fail to navigate this challenge due to their limited visibility below the pass/fail limits, discarding perfectly good chips or letting small defects slip through to the field. The challenge is clear: manufacturers must achieve both quality and yield goals without sacrificing one f... » read more

Using ML In Manufacturing


How to prevent early life failures by applying machine learning to different use cases, and how to interpret models for different tradeoffs on reliability. Jeff David, vice president of AI solutions at PDF Solutions, digs down into how to utilize data to improve reliability. » read more

Are All Known Good Tested Devices Created Equal?


Your known good parts all had passed their required wafer sort, final test, and system-level tests and were shipped to your customers. However, as we all know, a known good part or device sometimes does not stay good and may end up failing prematurely in the field and flagged as an RMA (return material authorization) by your customer. But why is it that some good parts fail early and others las... » read more

Outlier Detection


With increasing focus on quality and reliability across all segments beyond just automotive, medical and mil-aero, it is more critical than ever for companies to leverage every byte of test data at their disposal to ensure that they deliver the lowest possible DPPM (defective parts per million) rates to their customers. Semiconductor manufacturing operations now generate up to 100TB of test ... » read more