# Evaluating Modern Visual Anomaly Detection Approaches in Semiconductor Manufacturing: A Comparative Study

arXiv: https://arxiv.org/abs/2505.07576 · HTML: https://mlsanity.com/paper/2505.07576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 7 references that are in the map.
- First posted on arXiv 12 May 2025.

## Builds on (most cited first, 2 of 2)

- [CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization](https://mlsanity.com/paper/2206.04325/) (2022)
- [PaSTe: Improving the Efficiency of Visual Anomaly Detection at the Edge](https://mlsanity.com/paper/2410.11591/) (2024)

## Abstract (arXiv)

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.