Discovery of Resource-Oriented Transition Systems for Yield Enhancement in Semiconductor Manufacturing
Minsu Cho, Gyunam Park, Minseok Song, Jinyoun Lee, Byeongeon Lee, Euiseok Kum
Abstract
In semiconductor manufacturing, data-driven methodologies have enabled the resolution of various issues, particularly yield management and enhancement. Yield, one of the crucial key performance indicators in semiconductor manufacturing, is mostly affected by production resources, i.e., equipment involved in the process. There is a lot of research on finding the correlation between yield and the status of resources. However, in general, multiple resources are engaged in production processes, which may cause multicollinearity among resources. Therefore, it is important to discover resource paths that are positively or negatively associated with yield. This article proposes a systematic methodology for discovering a resource-oriented transition system model in a semiconductor manufacturing process to identify resource paths resulting in high and low yield. The proposed method is based on the model-based analysis (i.e., finite state machine mining) in process mining and statistical analyses. We conducted an empirical study with real-life data from one of the leading semiconductor manufacturing companies to validate the proposed approach.