Adaptive Test Gains Ground


Not all devices get tested the same way anymore, and that’s a good thing. Quality, test costs, and yield have motivated product engineers to adopt test processes that fall under the umbrella of adaptive test, which uses test data to modify a subsequent test process. But to execute such techniques requires logistics that support analysis of data, as well as enabling changes to a test based ... » read more

Combining Machine Learning With Advanced Outlier Detection To Improve Quality And Lower Cost


In semiconductor manufacturing, a low defect rate of manufactured integrated circuits is crucial. To minimize outgoing device defectivity, thousands of electrical tests are run, measuring tens of thousands of parameters, with die that are outside of specified parameters considered as fails. However, conventional test techniques often fall short of guaranteeing acceptable quality levels. Given t... » read more

How To Ensure Reliability


Michael Schuldenfrei, corporate technology fellow at OptimalPlus, talks about how to measure quality, why it’s essential to understand all of the possible variables in the testing process, and why outliers are no longer considered sufficient to ensure reliability. » read more

Who Is Responsible For Part Average Testing?


With ever-increasing demands in the automotive industry, more and more semiconductor companies are interested in monitoring and improving quality and reliability. Outlier detection and more specifically Part Average Testing (PAT) is the industry standard for the automotive industry. But, who is responsible for quality? Historically, OSATs are responsible for this. In the past, once they... » read more

Making AI More Dependable


Ira Leventhal, vice president of Advantest’s new concept product initiative, looks at why AI has taken so long to get going, what role it will play in improving the reliability of all chips, and how to use AI to improve the reliability of AI chips themselves. » read more

Finding Faulty Auto Chips


The next wave of automotive chips for assisted and autonomous driving is fueling the development of new approaches in a critical field called outlier detection. KLA-Tencor, Optimal+, as well as Mentor, a Siemens Business, and others are entering or expanding their efforts in the outlier detection market or related fields. Used in various industries for several years, outlier detection is one... » read more

Improving Yield, Reliability With Data


Big data techniques for sorting through massive amounts of data to identify aberrations are beginning to find a home in semiconductor manufacturing, fueled by new requirements in safety-critical markets such as automotive as well as the rising price of packaged chips in smartphones. Outlier detection—the process of finding data points outside the normal distribution—isn't a new idea. It ... » read more