[연구] 석사과정 차재경, SCI 논문지(MDPI applied sciences/Q2) 게재
- 스마트팩토리융합학과
- 조회수5887
- 2022-02-20
석사과정 차재경 학생(지도교수 : 정종필)의 연구(Improved U-Net with Residual Attention Block for Mixed-Defect Wafer Maps)가 MDPI applied sciences(Impact Factor: 2.679 (2020); 5-Year Impact Factor: 2.736 (2020))에 게재됐다.
https://doi.org/10.3390/app12042209 / https://www.mdpi.com/2076-3417/12/4/2209
논문요약 - Detecting defect patterns in semiconductors is very important for discovering the fundamental causes of production defects. In particular, because mixed defects have become more likely with the development of technology, finding them has become more complex than can be performed by conventional wafer defect detection. In this paper, we propose an improved U-Net model using a residual attention block that combines an attention mechanism with a residual block to segment a mixed defect. By using the proposed method, we can extract an improved feature map by suppressing irrelevant features and paying attention to the defect to be found. Experimental results show that the proposed model outperforms those in the existing studies.