EVOLUTION OF WAFER DEFECT DETECTION IN PHOTOLITHOGRAPHY: FROM CONVENTIONAL IMAGE PROCESSING TO DEEP LEARNING
DOI:
https://doi.org/10.35631/IJIREV.827001Keywords:
Deep Learning, Photolithography, Wafer DefectAbstract
The increasing complexity of semiconductor manufacturing and the continuous miniaturisation of integrated circuits have intensified the need for accurate, automated, and robust wafer defect detection in photolithography. Conventional image processing techniques have provided the foundation for automated inspection; however, their dependence on handcrafted features and predefined rules limits their adaptability to complex defect patterns, process variations, and high-volume manufacturing conditions. This study presents a chronological review of the technological evolution of wafer defect detection from conventional image processing to advanced deep learning approaches. A systematic literature search was conducted using the Scopus database, applying predefined search terms, inclusion criteria, and exclusion criteria to identify relevant primary research articles. A total of 37 studies published between 1989 and 2026 were selected and analysed according to their chronological development, inspection methodologies, computational approaches, and technological contributions. The literature was organised into three temporal phases: Phase I: Pioneering and Foundational Research (1989–2019), characterised by knowledge-based inspection, conventional image processing, handcrafted features, and early machine learning; Phase II: Transition toward Intelligent Defect Detection (2021–2022), marked by the increasing adoption of computer vision, machine learning, and convolutional neural networks; and Phase III: Rapid Advancement and Deep Learning Expansion (2023–2026), characterised by lightweight networks, attention mechanisms, transformer architectures, anomaly detection, synthetic data, multimodal learning, and real-time inspection. The findings demonstrate a clear shift from handcrafted feature engineering toward adaptive, data-driven, and integrated intelligent inspection systems. Nevertheless, challenges remain in dataset availability, model generalisation, false-positive reduction, computational efficiency, explainability, and high-volume manufacturing deployment. This review provides a chronological perspective of technological development and identifies key research gaps that can guide the development of robust, scalable, and industry-ready wafer defect detection systems for next-generation semiconductor manufacturing.
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