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.
- Artificial neural network