Rapidly growing market segments such as automotive and the IoT are driving the manufacturing of billions of connected devices around the globe. Moore’s Law is giving way to a More than Moore approach enabling companies to achieve new levels of performance and functionality that isn’t dependent on process node scaling. Both trends are driving enormous change for how companies collect and analyze their data to achieve product insights that improve performance, quality and reliability. As a result, the future of product test can no longer be limited primarily to test operations, and needs to be expanded to encompass both manufacturing and field usage data.
With the enormous computing power available today all the way out to the edge, the opportunities to apply AI and machine learning to broader and more comprehensive data sets will open up many new avenues for the electronics supply chain to drive quality to the part-per-billion levels needed for mission-critical systems such as autonomous driving. In addition, novel test structures embedded into silicon devices will become more commonplace as companies seek out new ways to complement existing machine-generated in order to identify reliability signatures faster and earlier at every phase of the product lifecycle, reducing early life failures and improving predictive maintenance approaches.
Dr. Kibarian will share how the nexus of semiconductor and AI/ML technologies are creating innovative solutions that are dramatically changing the future of the test landscape for the benefit of the entire electronics ecosystem.