An experiment of burn-in time reduction based on parametric test analysis

Burn-in is a common test approach to screen out unreliable parts. The cost of burn-in can be significant due to long burn-in periods and expensive equipment. This work studies the potential of using parametric test data to reduce the time of burn-in. The experiment focuses on developing parametric test models based on test data collected after 10 hours of burn-in to predict parts likely-to-fail after 24 and 48 hours of burn-in. Our study shows that 24-hour and 48-hour burn-in failures behave abnormally in multivariate parametric test spaces after 10 hours of burn-in. Hence, it is possible to develop multivariate test models to identify these likely-to-fail parts early in a burn-in cycle. This study is carried out on 8 lots of test data from a burn-in experiment based on a 3-axis accelerometer design. The study shows that after 10 hours of burn-in, it is possible to identify a large portion of all parts that do not require longer burn-in time, potentially providing significant cost saving.