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

Zihan Zhang

cs.CVMar 15, 2026

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

Authors: Zihan Zhang

Organizations: Engineering Technology Research Center of Henan Province for MEMS Manufacturing and Applications, School of Mechanics and Safety Engineering, Zhengzhou University, Zhengzhou 450001, China

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.

Figures & tables

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.

Layer	Input Size	Filter Size	Filter Num	Stride	Padding
Conv	$640\times 640$	$3\times 3$	64	2	1
Conv	$320\times 320$	$3\times 3$	128	2	1
C2f_MUSE	$160\times 160$	$\left[\begin{array}[]{c}1\times 1,64\\ 3\times 3,64\end{array}\right]\times 3$	128	1	1
Conv	$160\times 160$	$3\times 3$	256	2	1
C2f_MUSE	$80\times 80$	$\left[\begin{array}[]{c}1\times 1,128\\ 3\times 3,128\end{array}\right]\times 6$	256	1	1
Conv	$80\times 80$	$3\times 3$	512	2	1

Table 1: The parameters of the proposed TexWDS model.

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.

Figure 6: Illustration of the context structure within the proposed MUSE module, which contains three sub-branches: local, surrounding, and global channel attention.

Figure 7: Illustration of frequency decomposition, periodic disturbance extraction, and boundary-consistency attention within the proposed MPTCE module.

Figure 8: Normalized confusion matrix of the proposed Wafer defect detection framework, TexWDS.

P2 branch	MUSE	MPTCE	Group	Recall $\uparrow$	Precision $\uparrow$	mAP 50 $\uparrow$	mAP 50-95 $\uparrow$
✗	✗	✗	A0	71.2	71.4	72.7	47.9
$\checkmark$	✗	✗	A1	74.7	73.8	74.9	50.9
✗	$\checkmark$	✗	A2	74.6	76.2	75.7	50.3
✗	✗	$\checkmark$	A3	73.7	72.4	76.2	51.1
$\checkmark$	$\checkmark$	✗	A4	77.1	76.7	76.9	52.9
$\checkmark$	✗	$\checkmark$	A5	75.8	77.5	76.7	51.4

Table 6: Ablation Study Results on the Contributions of the Three Main Components of the TexWDS Model: P2 Branch, MUSE, and MPTCE. (Note:

\uparrow

indicates higher values are better.)

Figure 9: Comparison of feature activation maps in the final P3 detection layer between the baseline model and TexWDS. (a) Original image, (b) YOLOv8, and (c) TexWDS (Ours).

Figure 10: Visualization of periodic disturbance maps generated by the MPTCE module. (a) represents the original image, (b) represents the effect of overlaying the heatmap on the original image, and (c) represents the pure heatmap.

Figure 11: Comparison of detection results. (a), (b) and (c) represent the manually annotated defect bounding boxes and the detection results of YOLOv8 and TexWDS, respectively. Red boxes indicate detected defects, black boxes indicate detection failures.

Figure 12: Comparison of Segmentation Results. (a), (b) and (c) denote the manually annotated defect mask, the segmentation result of YOLOv8, and the segmentation result of TexWDS, respectively.

Image Segmentation

Image segmentation assigns pixels or voxels to objects and regions, supporting measurement, scene understanding, and decisions across image domains.

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Open paper AskPDF arXiv

In semiconductor manufacturing, defect analysis is essential, but manual inspection cannot scale. However, existing automated inspection methods remain insufficient for root-cause analysis and process optimization. To this end, we present SePArate, a weakly supervised wafer defect segmentation method. SePArate enables pixel-level separation of patterns by leveraging only image-level annotations. It consists of a three-phase training: encoder pretraining, knowledge transfer to learn spatial cues, and training on synthetic mixed-defect data for accurate segmentation. Experiments demonstrate that SePArate outperforms the baselines.

Abstract

Image Segmentation

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