Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study
This work addresses the need for cost-effective anomaly detection in semiconductor manufacturing, but it is incremental as it applies existing methods to a new benchmark.
The study tackled the problem of automated visual inspection in semiconductor manufacturing by evaluating modern visual anomaly detection approaches on the MIIC dataset, demonstrating their efficacy in this domain.
Semiconductor manufacturing is a complex, multistage process. Automated visual inspection of Scanning Electron Microscope (SEM) images is indispensable for minimizing equipment downtime and containing costs. Most previous research considers supervised approaches, assuming a sufficient number of anomalously labeled samples. On the contrary, Visual Anomaly Detection (VAD), an emerging research domain, focuses on unsupervised learning, avoiding the costly defect collection phase while providing explanations of the predictions. We introduce a benchmark for VAD in the semiconductor domain by leveraging the MIIC dataset. Our results demonstrate the efficacy of modern VAD approaches in this field.