Prediction of Electrical and Physical Failure Analysis Success Using Artificial Neural Networks

It is well known that fail dies that exhibit obvious static power supply leakage current have a higher success of finding a defect, hence, a higher likelihood to be selected for failure analysis. When confronted with choices, fail dies with supply current similar to reference will be omitted. Valuable defect learnings are lost as a result. In this work, an artificial neural network is developed to perform the task of predicting failure analysis success confidence level on fail dies. The input parameters are dynamic current measured from multiple power supplies and an output accuracy of 80% on average is achieved. Besides an enhanced productivity from automating the dies selection process, more importantly, fail dies which are otherwise neglected, can be identified. This first demonstration is a new milestone for the application of machine learning to the failure analysis domain.