Benchmarking Feature Extractors for Reinforcement Learning-Based
  Semiconductor Defect Localization
Enrique Dehaerne Bappaditya Dey Sandip Halder Stefan De Gendt
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.