Automatic Defect Classification (ADC) solution using Data-Centric Artificial Intelligence (AI) for outgoing quality inspections in the semiconductor industry

In this study, we present an automatic defect classification (ADC) application for outgoing quality inspections. In most outgoing inspections, all of the defects were manually classified to reject or accept the inspected die with the defect classification. Earlier adoption of ADC systems usually emphasizes both accuracy (recall) and purity (precision) as output metrics to deploy the system to classify the defects. In our implementation, purity is targeted as the main output metric for the classification of clearly defined defects in the training set. This allowed us to deploy an automatic defect classification solution with high purity and benefit from its automatic classification earlier in the adoption process with an immediate impact on workload reduction, while progressively tuning performance on less pure defect classes. Overall, higher than 80% purity levels are achieved on more than 75% of the population of all the defects assigned for classification. Several ad-hoc monitoring systems; such as time-window based statistical tests and subject-matter-expert (SME) based performance of ground truth, are implemented for the continuity of the performance of the classifier.