Neural network based yield prediction models are developed to optimize high-speed microprocessor manufacturing processes.Based on measured sirly EI' (electrical test) data, wafer level parametric yield prediction models are developed In this work, manufocturing yield was considered as a manUfacturing performance index because it is very critical to overall manufacturing cost and product quality. The prediction results show 41.09% improvement as compared to statistical prediction model using multiple regression. These modeling approaches are also applied to predict final chip speed to minimize undesirable packaging costs. The prediction results show only 1.7% of average speed diffe rences. Ultimately, these neural prediction models are used to find optimal process conditions, and with the successful implementation of this work, it can serve as a catalyst to improve productivity and chip quality.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.