Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization
This work addresses the need for advanced defect inspection techniques in semiconductor manufacturing, presenting an incremental improvement by benchmarking feature extractors within an RL framework.
The paper tackles semiconductor defect localization by proposing a deep Reinforcement Learning approach that iteratively extracts features from smaller image regions, comparing 18 agents with different feature extractors to evaluate their performance.
As semiconductor patterning dimensions shrink, more advanced Scanning Electron Microscopy (SEM) image-based defect inspection techniques are needed. Recently, many Machine Learning (ML)-based approaches have been proposed for defect localization and have shown impressive results. These methods often rely on feature extraction from a full SEM image and possibly a number of regions of interest. In this study, we propose a deep Reinforcement Learning (RL)-based approach to defect localization which iteratively extracts features from increasingly smaller regions of the input image. We compare the results of 18 agents trained with different feature extractors. We discuss the advantages and disadvantages of different feature extractors as well as the RL-based framework in general for semiconductor defect localization.