MEMFD: A Multi-EDT Multi-Fault Scan Chain Diagnosis Methodology with Deep Learning

Manufacturing defects need to be detected and diagnosed with the help of on-chip test infrastructure. Scan chains are the most important part of the test infrastructure in the modern designs. However, these scan chains also turn out to be crucial defect locations requiring a diagnosis procedure that has severe implications on debug time and root-cause discovery. These problems are further exacerbated by the compression mechanism present in the test infrastructure. Diagnosis methods proposed in the literature mainly suggest techniques based on fault type-based dictionary computation, simulation with input diagnostic patterns and the subsequent analysis of error responses against the inputs. However, these methods do not take into account the advanced test compression infrastructure. In this work, we propose a diagnosis methodology that is based on state-of-the-art multi-embedded deterministic test (EDT) architecture. We utilize deep learning-based analysis methodology for the purpose of identification of the faulty EDT section based on the observed output responses. Experimental results indicate that with lesser overhead in debug time and resources, the proposed methodology can diagnose faulty EDT with a success rate of 85-90%.