• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
Title Development of a Multi-Defect Pattern Classification Model for Wafer Maps Using Deep Learning
Authors 이지현(Ji-hyeon Lee) ; 조현종(Hyun-chong Cho)
DOI https://doi.org/10.5370/KIEE.2025.74.6.1115
Page pp.1115-1121
ISSN 1975-8359
Keywords Deep Learning; Defect Analysis; Multi-Defect; Semiconductor; Wafer Map
Abstract In semiconductor manufacturing, defects on wafer maps often arise due to process-related issues. Recognizing and analyzing these defect patterns is crucial for the early detection of manufacturing faults, enabling proactive defect prediction and ultimately enhancing semiconductor yield rates. In real-world manufacturing, wafers commonly exhibit not only singular defects but also multiple concurrent defects, adding complexity to the analysis. Therefore, this study proposes a deep learning-based model for wafer map multi-defect pattern classification. We employ the ViT-B model for classification and utilize TrivialAugment to increase the diversity of wafer map images used in training. Additionally, two optimizers were applied to facilitate the smooth convergence of the model's loss function, allowing for a comparative performance evaluation. On the MixedWM38 dataset, which comprises 38 classes, our model enhanced with TrivialAugment and Adagrad achieved F1-scores of 0.984 for single defects, 0.987 for double defects, 0.982 for triple defects, and 0.986 for quadruple defects.