Multi-Period Texture Contrast Enhancement for Low-Contrast Wafer Defect Detection and Segmentation

2026-03-15

AI Summary

This work addresses the challenges in wafer defect detection where subtle, low-contrast defects are often obscured by strong periodic backgrounds, and existing deep learning approaches suffer from feature dilution due to downsampling and difficulty distinguishing process-induced noise from genuine defects. To overcome these limitations, the authors propose the TexWDS framework, which explicitly decouples aperiodic defects from periodic textures through multi-scale receptive field reweighting, a Multi-scale Unified Semantic Enhancer (MUSE), and a plug-and-play Multi-Periodic Texture Contrast Enhancement (MPTCE) module. By integrating frequency-domain perturbation modeling with multi-scale feature preservation, TexWDS achieves state-of-the-art performance on industrial datasets, yielding an 8.3% improvement in mAP50-95, a 7.7% increase in recall, and an 8.6% reduction in false positive rate.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Other Foundations of Computer Vision
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Wafer defect segmentation is pivotal for semiconductor yield optimization yet remains challenged by the intrinsic conflict between microscale anomalies and highly periodic, overwhelming background textures. Existing deep learning paradigms often falter due to feature dilution during downsampling and the lack of explicit mechanisms to disentangle low-contrast defects from process-induced noise. To transcend these limitations, we propose TexWDS, a texture-aware framework that harmonizes multi-scale feature retention with frequency-domain perturbation modeling. Our methodology incorporates three strategic innovations: (1) A Multi-scale Receptive Field Reweighting strategy is introduced to mitigate aliasing effects and preserve high-frequency details of micro-defects often lost in standard pyramidal architectures. (2) The Multi-scale Unified Semantic Enhancer (MUSE) integrates local appearance with global context encoding, effectively enhancing feature discriminability in low-visibility regions. (3) Crucially, we design a plug-and-play Multi-Periodic Texture Contrast Enhancement (MPTCE) module. By modeling texture disruptions in the frequency domain, MPTCE explicitly decouples non-periodic anomalies from structured backgrounds, boosting contrast for camouflaged defects. Extensive experiments on real-world industrial datasets demonstrate that TexWDS achieves a new state-of-the-art, surpassing the baseline by 8.3% in mAP50-95 and 7.7% in recall, while reducing the false positive rate by approximately 8.6%. These results underscore the framework's robustness in handling complex periodic patterns and its suitability for high-precision manufacturing inspection.

Problem

• wafer defect segmentation
• low-contrast defects
• periodic background textures
• feature dilution
• process-induced noise

Innovation

• Multi-scale Receptive Field Reweighting
• MUSE
• MPTCE
• frequency-domain perturbation modeling
• texture-aware defect segmentation