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

Zihan Zhang

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.

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

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.
Paper Structure (17 sections, 16 equations, 12 figures, 6 tables)

This paper contains 17 sections, 16 equations, 12 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Conventional Wafer Inspection Algorithms
  4. Deep Learning-Based Generic Detection
  5. Context-Aware and Texture-Specific Modeling
  6. Method
  7. Multi-scale Receptive Field Reweighting
  8. Multi-scale Unified Semantic Enhancer with Context Encoding (MUSE)
  9. Multi-Periodic Texture Contrast Enhancement (MPTCE)
  10. Experiment and Analysis
  11. Dataset
  12. Implementation Details
  13. Evaluation Metrics
  14. Quantitative Results
  15. Ablation Study
  16. ...and 2 more sections

Figures (12)

  • Figure 1: Examples of scenarios where existing deep learning models perform suboptimally.
  • Figure 2: Normalized confusion matrix of YOLOv8-seg (baseline).
  • Figure 3: Schematic overview of the YOLOv8-seg (baseline) architecture.
  • Figure 4: Schematic overview of the proposed Texture-Aware Wafer Defect Segmentation (TexWDS) architecture, showing the integration positions of Stage 1 (P2 branch), Stage 2 (Multi-scale Unified Semantic Enhancer with Context Encoding (MUSE), and Stage 3 (Multi-Period Texture Contrast Enhancement, MPTCE) within the backbone--neck pipeline.
  • Figure 5: Comparison of feature maps from detection heads at different scales. (a), (b) and (c) represent the feature maps of the YOLOv8 model at the P2, P3, and P4 scales, respectively.
  • ...and 7 more figures