An ANN approach for modeling the multisource yield learning process with semiconductor manufacturing as an example

Abstract

Estimating the future yield of a product is a crucial task for semiconductor manufacturers. However, existing methods cannot differentiate the effects of various sources of yield improvement. To address this concern, this study proposes an innovative approach for modeling the yield learning process of a semiconductor product with artificial neural networks, which enable separating the effects of various sources of yield learning. Two real cases were used to demonstrate the effectiveness of the proposed methodology. (C) 2016 Elsevier Ltd. All rights reserved.