LearnX: A Hybrid Deterministic-Statistical Defect Diagnosis Methodology
I presented this paper at the European Test Symposium (ETS), 2019.
Authors: Soumya Mittal, Shawn Blanton
Summary: This paper describes a two-phase, physically-aware diagnosis methodology called LearnX to improve the quality of diagnosis, and in turn the quality of design, test and manufacturing. The first phase attempts to diagnose a defect that manifests as a well-established fault behavior (e.g., stuck or bridge fault models). The second phase uses machine learning to build a model (separate for each defect type) that learns the characteristics of defect candidates to distinguish correct candidates from incorrect ones.