By Paula Doe

Chip testing is becoming smarter and more complex, creating growing requirements to stream data in real time and ensure it is ready to use for analysis, regardless of the vendor source.

Adaptive testing using machine learning to predict die performance in a downstream test can reduce the number of cycles by as much as 40 per cent without compromising test performance, notes Dan Sebban, VP of data analysis, OptimalPlus, who’ll speak on machine learning challenges at SEMICON West’s Test Vision 2020 program. “As devices and their test requirements grow in complexity, the motivation for automating adaptive test greatly increases,” he states, adding that characteristics such as die location on the wafer, defects on neighboring die, condition of the tester, and test values near the specification limits can help predict which die are likely to be good.

“The big issue we see is that while everyone likes the idea of machine learning, it remains a black box model, with little visibility into why it makes the decisions it does,” adds Sebban. In addition, a suitable infrastructure to run, deploy and assess a machine learning model in real time is required. “There is still some hesitation to adopt machine learning. It’s a big change of mindset. While building the confidence to use machine learning will take time and experience, using the technology to automate big data analysis with the relevant infrastructure may be our best alternative to reduce test cost.”

Systems test and parts-per-billion quality become the rule

Systems test will continue to become more prominent and more complex as chips and packages shrink, affirms Stacy Ajouri, Texas Instruments system integration engineer and Test Vision 2020 event chair. “Even IC makers now need to start doing more systems test.” And as more ICs are used in automotive applications, the distinction between consumer and automotive requirements is blurring, driving demand in other markets for higher precision test with parts-per-billion defectivity requirements.

“Intelligent test gets increasingly challenging as devices become more complex and as testing moves from distinguishing good from bad devices to figuring out how to repair and trim marginal devices to make them good,” adds Derek Floyd, Advantest director of business development, this year’s program chair.

“We’re highlighting efforts to create the infrastructure the industry needs to manage big data for machine learning with test platforms from different vendors,” says Ajouri, citing work on new standards for streaming data from the testers and labeling critical steps in consistent language to simplify the use of data from different platforms in real time. “I have 10 platforms from multiple vendors, and I need them to mean exactly the same thing by ‘lot’ so I don’t have to sort it out before I can use the data,” she says.

Are devices becoming too complicated to test at the required price point?

Can testing be economical with up to a million die per wafer, 50 data points per die, a requirement for parts-per-billion accuracy, and the need to identify parts that test good now but that might fail in the future? Organizers of the event invite chipmakers and test suppliers to debate the issue. “The speed of innovation in the semiconductor industry challenges test to keep pace,” notes Floyd. “The product we’re testing is always ahead of the product we have to test it with.”

The two-day event features sessions on automotive test; big data and machine learning for adaptive test; handling and interface issues such as over-the-air testing;  and a general session covering memory and RF test.