The paper introduces a morphology‑aware ambiguity learning framework for wafer defect decision support. It enables three diagnostic actions—automatic single‑class diagnosis, assisted diagnosis with two plausible defect classes, and full review—by constructing a class‑level ambiguity matrix from wafer map characteristics. Experiments on the WM‑811K dataset demonstrate that the framework outperforms traditional methods, offering meaningful two‑class alternatives and reserving full review for truly ambiguous cases, with consistent performance across different backbone architectures.
By Seungjun Chu, Seokhyun Chung
The paper introduces a hybrid one‑class anomaly detector for semiconductor wafer bin maps that combines a patch‑based student‑teacher model (EfficientAD) with a denoising diffusion probabilistic model (DDPM). Trained on only 700 normal wafers, the fused system achieves an AUROC of 0.9985 on 18,658 held‑out wafers, reducing misclassifications from 852 (DDPM) and 1,412 (EfficientAD) to 618, with statistically significant improvements. Analysis reveals that the two modules make weakly overlapping errors, and that fixed‑weight fusion captures only 40‑70 % of the potential correction, highlighting the need for sample‑adaptive fusion and imbalance‑aware evaluation.
By Limon Bin Hossain, Md Sadib Rahman Ananta
arXiv:2610.19540v1 Announce Type: new
Abstract: Automated defect detection of industrial boards must remain highly reliable under cross-scenario variations in lighting, surface contaminants, and prod...
By Dikai Li, Weijia Wang, Yuan-Gen Wang
SePArate is a weakly supervised method for segmenting patterns from defects in semiconductor wafers. It uses only image‑level annotations and follows a three‑phase training process: encoder pretraining, knowledge transfer for spatial cues, and training on synthetic mixed‑defect data. Experiments show that SePArate outperforms baseline approaches in pixel‑level defect segmentation.
By Dain Kwon, Changmin Shin, Sunjong Park, Kanghyun Choi, Hyeyoon Lee, Jaewon Jang, Minseok Choi, Jinho Lee
arXiv:2609.35059v2 Announce Type: replace
Abstract: 3D anomaly detection (3DAD) aims to identify defective regions in point cloud data, serving as a critical component in industrial inspection system...
By KunHo Heo, SuYeon Kim, Hayoung Lee, Chanse Oh, MyeongAh Cho
The paper introduces ISP-AD, the largest publicly available industrial anomaly detection dataset, featuring both synthetic and real defects from a factory floor. It focuses on challenging, small, weakly contrasted surface defects within highly variable structured patterns, addressing the bias of existing datasets toward ideal imaging conditions. Experiments demonstrate that even a small amount of weakly labeled real defects improves model generalization and that synthetic defects can serve as a useful cold‑start baseline for scalable training.
By Paul J. Krassnig, Dieter P. Gruber
arXiv:2608. 07770v1 Announce Type: new Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial conditions is less clear.
By Mike Szklarzewski, CJ George, Gavin Smithson, Christopher Stokes, Dakota Fulp, William M. Jones, Benjamin Wynn, Alexander Ur, Agit Yesiloz, Clint Kallenbach, Mark Swartz, Nathan DeBardeleben, Sharmistha Chakrabarti
arXiv:2605. 24251v2 Announce Type: replace Abstract: Continual anomaly detection (CAD) addresses the need for industrial inspection systems to adapt to evolving production conditions, yet existing methods share three critical gaps: unrealistic evaluation, no systematic comparison, and no consideration of edge deployment constraints.
By Chad Weatherly, Sen Lin
This paper presents the IEEE International Conference on Multimedia and Expo (ICME) 2026 Grand Challenge on Cross-Scenario Defect Detection and Fine-Grained Severity Grading for High-Precision Manufacturing. The challenge is motivated by two key limitations of existing industrial defect inspection systems: (1) current deep learning-based methods often suffer significant performance degradation when deployed in unseen production scenarios, and (2) most benchmarks neglect severity-aware assessment, which is critical for risk control and yield optimization.
arXiv:2606. 13754v1 Announce Type: new Abstract: Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments.
By Ghazal Ghajari, Elaheh Ghajari, Ashutosh Ghimire, Saeid Ataei, Faris Alsulami, Fathi Amsaad
arXiv:2509. 22267v5 Announce Type: replace Abstract: Reliable detection of bearing faults is essential for maintaining the safety and operational efficiency of rotating machinery.
By Jo\~ao Paulo Vieira, Victor Afonso Bauler, Rodrigo Kobashikawa Rosa, Danilo Silva
The paper introduces FuDU, a streaming active learning framework that enhances real‑time industrial defect detection by quantifying uncertainty at both image and box levels. It employs a Prototype-based Global Uncertainty Quantification module to assess image‑level uncertainty and a Dual‑entropy defect Uncertainty Evaluator for box‑level uncertainty. By fusing these uncertainties through fuzzy inference, FuDU enables expert‑knowledge‑driven adaptive sampling, improving reliability in tasks such as nuclear fuel rod defect detection.
By Zhaoyang Wang, Haiyong Chen, Binyi Su, Xinwei Lyu