EDACafe Industry Predictions for 2024 – Advantest

By Ken Butler, Business Development Senior Director, Advantest Cloud Solutions (ACS)

Ken Butler

Most semiconductor market sectors saw a softening of business in 2023. However, 2024 should herald a rebound in demand. One driver will be the U.S. CHIPS and Science Act, which has already awarded its first contract as of this writing, and the similar efforts in other countries, each intended to minimize reliance on foreign sources within their respective semiconductor supply chains. Another part of that increase will be attributed to recent significant growth in the use of artificial intelligence (AI). In day-to-day life, there are many examples, such as AI-powered text and image generation, large language model (LLM)-enabled search engines from all the major providers, and AI productivity enhancers for many endeavors in STEM fields and others.

AI in Semiconductor Test

Semiconductor test is no exception to the infusion of AI technology into workflows and will also benefit from these new advances. In late 2023, Advantest announced the availability of the ACS Real-Time Data Infrastructure (RTDI), fueled by customer demand for adding complex AI/ML analytics to their test solutions. For all modern semiconductor products, but particularly for highly integrated “More than Moore” chiplet-based designs, product teams will continue to look for ways to incorporate advanced inferencing into the test process to optimize this valuable resource. Test optimization will be a frequent goal – every device should see “the right” test content – no more and no less than it needs. But today’s neural network-based techniques will also be used to detect devices that are quality or reliability risks, and should either be scrapped or have additional screening applied to ensure they will meet the demands of the end application and do so throughout the expected lifetime. Yield improvement will be another target, and AI will be applied to maximize product learning during the initial product ramp and into high-volume manufacturing. These inferences will increasingly be written in the languages favored by data scientists, such as Python, and made to run in concert with a test program written in a more traditional programming language.

Real-Time AI Analytics

Some of these analytical methods will be applied in a “post-test” fashion as has been done frequently in the past, such as at the end of wafer sort and before the wafers move on to the assembly/test operation. But increasingly, AI will need to be done in real-time, as the devices are tested, such as at package test or system-level test, and inferences computed and acted upon with very low latency. So, IC manufacturers will turn to edge-computing solutions, where the test data is processed immediately as it is being generated. The data that feeds the AI models in these applications might also need to come from other sources, such as upstream test insertions, the wafer fabrication facility, and other places. Speedy and secure data feed forward mechanisms will be employed to facilitate integration and use of data from a variety of origination points.

AI in Test Development

Another part of the semiconductor test ecosystem that will be impacted by AI methods is the development of the test program itself. At its core, a test program, as the name implies, is a piece of code with the specialized purpose of governing the execution of a sequence of tests that verify the functionality of an IC. We have already seen how generative AI is being applied to the code development process to increase software developers’ productivity. Test code development will see similar benefits in 2024. One of the challenges to deploying such a solution will be in training an LLM with a sufficiently large enough example code database to make it effective for end users. Most large IC houses and ATE suppliers likely have access to libraries and repositories of code that can be used for this purpose. There will still be a human “in the loop,” but the productivity boost will be substantial.

Conclusions

In 2024, AI will increase its footprint across many aspects of semiconductor testing. The end result will be big gains in efficiency, effectiveness, and test development speed. The earlier adopters of these technologies will enjoy more advantageous positions in the marketplace. These new capabilities will further strengthen the notion that the test process is not just a necessary step in chip production but truly adds value to the product. It will be an exciting time indeed to be a product and test engineer. And in case anybody is wondering, this article was written by a human and not by ChatGPT or another AI tool.

About the Author:

Ken Butler is a senior director of business development in the ACS data analytics platform group at Advantest. Prior to that, he worked for 36 years at Texas Instruments in DFT and test generation, semiconductor reliability, analog product and test engineering, and data analytics. Ken has a BS from Oklahoma State University and an MS and PhD from the University of Texas at Austin, all in electrical engineering. He is a Fellow of the IEEE, a Golden Core member of the IEEE Computer Society, and a senior member of the ACM.

Category: Predictions

This entry was posted on Friday, January 12th, 2024 at 1:56 pm. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.