Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study
By: Manuel Barusco , Francesco Borsatti , Youssef Ben Khalifa and more
Potential Business Impact:
Finds tiny flaws in computer chips automatically.
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.
Similar Papers
Semiconductor SEM Image Defect Classification Using Supervised and Semi-Supervised Learning with Vision Transformers
CV and Pattern Recognition
Finds tiny flaws in computer chips automatically.
On-Device Continual Learning for Unsupervised Visual Anomaly Detection in Dynamic Manufacturing
Machine Learning (CS)
Finds factory flaws faster on old machines.
From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge
CV and Pattern Recognition
Finds weird things in pictures faster.