Dynamic Environmental Stress Screening Using Machine Learning

Thermal Environmental Stress Screening (ESS) is a proven method used to detect manufacturing defects in production hardware. Numerous multi-hour cycles are performed to properly screen systems with thousands of solder connections, complex mechanical configurations, and intricate electrical designs. The current industry standard thermal ESS process is to perform a survey on the system, define the profile, and establish a set quantity of cycles to perform per system. It is also well-understood that machine learning has the capability to improve manufacturing processes [1]. In an effort to reduce test times and unnecessary stress, a Machine Learning (ML) model, based on the amount of production rework performed on the system prior to ESS, can be generated to predict the optimal amount of cycles to perform on the system. This approach improves both cost and schedule of the system under test.