Work

Wafer Defect Recognition for Industrial Inspection: FCS-VMamba Model and Experimental Validation

Year: 2026

Type: article

Abstract: In industrial imaging scenarios, semiconductor wafer defect classification is crucial for chip manufacturing yield and reliability. However, numerous challenges persist, including weak imaging responses and detail loss during downsampling, complex backgrounds that interfere with feature extraction, and the trade-off between performance and efficiency on edge devices. Traditional CNNs and ViTs exhibit limitations in modeling long-range dependencies and managing edge deployment costs. To address these issues, we leverage the VMamba architecture, a Visual State Space Model (SSM) that achieves global contextual modeling with linear computational complexity. Based on the VMamba architecture, we propose FCS-VMamba, a domain-adapted model that integrates three core modules, namely Frequency Attention (FA), Cross-Layer Cross-Attention (CLCA), and Saliency Feature Suppression (SFS). The experimental results show that FCS-VMamba achieved 86.06% macro-precision and 87.91% Top-1 accuracy with only 1.2 M parameters. These results demonstrate that FCS-VMamba provides a practical and parameter-efficient baseline for industrial wafer defect recognition.

Source: Journal of Imaging

DOI: 10.3390/jimaging12040142

Authors: Yijia Zhang, Ziyi Ma, Tongji Cui, Tiejun Zhao, Qi Wang, Jianhua Wang

Institutions: Hebei University of Technology, Xuzhou Construction Machinery Group (China), Inner Mongolia University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,172

Funders: China Postdoctoral Science Foundation, Natural Science Foundation of Hebei Province