Positive/Negative Decision via Outlier Detection Toward Automatic Performance Evaluation for Defect Detector

In the field of semiconductor defect inspection, detecting defects with high accuracy is possible owing to the object detection model (defect detector) composed of the deep learning model. On the other hand, the performance of the deep learning model depends highly on training data. Therefore, during the operational phase at the customer site, we need to frequently evaluate the model’s performance to deal with shifts in appearance for defects. However, frequently executing general evaluation methods is difficult at the customer site. Hence, we need a method to automatically evaluate performance. In this study, for the purpose of automatically evaluating the performance of the defect detector, we propose Positive/Negative Decision via Outlier Detection (PNDOD). The PNDOD decides on the positive of negative for each detection result based on comparing features corresponding to the detected result with statistics computed from training data. Using this method, we can calculate the estimated precision from the ratio of the estimated number of positive detections to the number of total detections, and we can evaluate the performance automatically based on it. Through experiments, we confirm the PNDOD can decide on the positive or negative with high accuracy, and we can precisely evaluate the model’s performance.