Abstract
This paper introduces the use of machine learning models in the characterization of bitmap fail patterns occurring on SRAM to identify FEOL/MEOL layers defectivity distribution. The results of bitmap patterns with test conditions are used for fault analysis post-processing and manufacturing yield improvement methodologies. Several machine learning models were built for prediction of the FEOL/MEOL layer defects based on hundreds of bitmap physical failure analysis results. A model utilizing a multilayer perceptron (MLP) architecture with backpropagation of error were optimized and it can be easily applied to volume products with millions of bitmap test results with >80% accuracy. It is the first time we are able to investigate the FEOL/MEOL defects density quantitatively through an automatic diagnosis tool.
