mlsanityData to 14 Oct 2026

Image-Intrinsic Priors for Integrated Circuit Defect Detection and Novel Class Discovery via Self-Supervised Learning

arXiv 2511.03120 ↗First posted 5 Nov 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Integrated circuit manufacturing is highly complex, comprising hundreds of process steps. Defects can arise at any stage, causing yield loss and ultimately degrading product reliability. Supervised methods require extensive human annotation and struggle with emergent categories and rare, data scarce defects. Clustering-based unsupervised methods often exhibit unstable performance due to missing priors. We propose IC DefectNCD, a support set free framework that leverages Image Intrinsic Priors in IC SEM images for defect detection and novel class discovery. We first develop Self Normal Information Guided IC Defect Detection, aggregating representative normal features via a learnable normal information extractor and using reconstruction residuals to coarsely localize defect regions. To handle saliency variations across defects, we introduce an adaptive binarization strategy that produces stable subimages focused on core defective areas. Finally, we design Self Defect Information Guided IC Defect Classification, which incorporates a soft mask guided attention mechanism to inject spatial defect priors into the teacher student model. This enhances sensitivity to defective regions, suppresses background interference, and enables recognition and classification of unseen defects. We validate the approach on a real world dataset spanning three key fabrication stages and covering 15 defect types. Experiments demonstrate robust performance on both defect detection and unseen defect classification.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 55 references that are in the map. First posted on arXiv 5 Nov 2025.

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37 of 55 references.

References · 37

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25 more
  1. 2021A Unified Objective for Novel Class Discoverycited by 162
  2. 2023DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion Modelscited by 162
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  1. 2024Addressing Class Imbalance and Data Limitations in Advanced Node Semiconductor Defect Inspection: A Generative Approach for SEM Images
  2. 2026Diffuse to Detect: Generative Diffusion Models for Unsupervised IC Anomaly Detection
  3. 2025Exploring "Many in Few" and "Few in Many" Properties in Long-Tailed, Highly-Imbalanced IC Defect Classification
  4. 2025Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics
  5. 2026Efficient Patch-Based Anomaly Detection Fused with Diffusion Driven Generative Modeling for Semiconductor Wafer Bin Map Open Set Anomaly Detection
  6. 2025From Lab to Factory: Pitfalls and Guidelines for Self-/Unsupervised Defect Detection on Low-Quality Industrial Images
  7. 2023Wafer Map Defect Patterns Semi-Supervised Classification Using Latent Vector Representation
  8. 2025ExDD: Explicit Dual Distribution Learning for Surface Defect Detection via Diffusion Synthesis