Feature selection and feature design for machine learning indirect test: a tutorial review

Machine learning indirect test replaces costly specification measurements by simpler signatures and use modern learning algorithms to map these signatures to specifications. Defining a set of relevant signatures that appropriately captures the circuit performance degradation mechanisms is then a key point for enabling machine learning indirect test. In this tutorial we review some methodologies for selecting and designing such a set of information rich signatures.