반도체 계측 데이터 기반 군집화를 활용한 개선된 품질 예측 방법론

Abstract

Various methods have been applied to guarantee and improve the quality of products in the semiconductor manufacturing. However, defects are becoming more various and difficult to control with product diversification and technology advancement. To ensure and improve the quality with productivity, this study predicted the final quality with actual semiconductor manufacturing data generated in each process of various characteristics. To improve the performance with practicality, failure occurrence environment and data characteristics should be considered. As the technology complexity increases, the defect frequently occurs with a same phenomenon but different root cause. Therefore, we proposed the system that divides defect types by characteristics and predict quality with unsupervised learning such as k-means and SOM (Self-Organized Map). The proposed method could provide an individual clue to improvements by clustering characteristics for defects. In addition, it showed verified applicability by improving performance about 4.4%p in AUC (Area Under the ROC Curve) and 6.8%p in partial AUC.