For manufacturers, forecasting the future yield of a product is a critical task. However, a yield learning process involves considerable uncertainty, rendering the task difficult. Although a few fuzzy collaborative intelligence (FCI) methods have been proposed in recent years, they are not problem-free. Hence, to overcome the challenges associated with these methods and to improve the accuracy of future yield forecasts, a heterogeneous FCI approach is proposed in this study. In this method, an expert applies mathematical-programming-based or artificial-neural-network-based methods (i.e., heterogeneous methods) to model an uncertain yield learning process. Subsequently, fuzzy intersection narrows the possible range of the future yield, and finally, an artificial neural network derives a crisp (representative) value. The effectiveness of the proposed heterogeneous FCI approach was successfully demonstrated by considering data obtained from a factory manufacturing dynamic random access memory devices. The approach achieved an average increase of 21% in the forecasting accuracy compared with existing approaches.
- Fuzzy collaborative intelligence