DEEP LEARNING FOR WAFER DEFECT DETECTION AND CLASSIFICATION IN SEMICONDUCTOR PHOTOLITHOGRAPHY: A STRUCTURED REVIEW
DOI:
https://doi.org/10.35631/IJIREV.827003Keywords:
Defect, Deep Learning, Detection, Photolithography, SemiconductorAbstract
The increasing complexity of semiconductor manufacturing and photolithography processes has intensified the need for accurate, efficient, and scalable wafer defect detection and classification. Conventional inspection approaches may face challenges in identifying complex, low-contrast, mixed-type, and previously unseen defect patterns, creating a growing need for deep learning-based solutions that can support automated and intelligent inspection. This systematic literature review examines the development of deep learning approaches for wafer defect detection and classification in semiconductor photolithography, with particular emphasis on emerging architectures, learning strategies, and industrial implementation. The review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. An advanced search strategy was applied to the Scopus and IEEE Xplore databases using the core keywords defect, detection, semiconductor, deep learning, and photolithography, covering publications from 2019 to October 2026. Following identification, screening, duplication removal, eligibility assessment, and quality evaluation, 15 primary studies were retained for qualitative synthesis. The findings were systematically organized into three themes: Theme 1, Lightweight Network Topologies and Real-Time Object Detection, highlighting computationally efficient architectures and real-time inspection capabilities; Theme 2, Self-Supervised Representation, Vision–Language Alignment, and Data-Efficient Adaptation, emphasizing approaches that address limited labelled data and improve model adaptability; and Theme 3, Industrial AOI Hardware Integration, Multi-Scale Segmentation, and Benchmark Evaluations, focusing on deployment-oriented inspection systems, precise defect localization, and standardized performance assessment. Collectively, the findings indicate a research trajectory from increasingly efficient deep learning architectures toward data-efficient learning and industrially integrated inspection frameworks. The review provides a structured synthesis of current developments and identifies key methodological directions for advancing reliable, scalable, and intelligent wafer defect inspection in semiconductor photolithography.
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