Forecasting the yield of a semiconductor product with a collaborative intelligence approach

In this study, a collaborative intelligence approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more reliable.

Abstract

Forecasting the yield of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the yield. In order to effectively forecast the yield, a collaborative intelligence approach is proposed in this study. The difference with the existing methods is that the collaborative intelligence approach takes into account the different points of view in a more efficient way, and therefore the results obtained are more comprehensive and more reliable. In the collaborative intelligence approach, a group of domain experts is formed. These domain experts are asked to configure their fuzzy feed-forward neural networks (FFNNs) to forecast the yield based on their views. A collaboration mechanism is therefore established to evolve the views. To facilitate the collaboration process and to derive a single representative value from these forecasts, the maximal-consensus and radial basis function network (MC-RBF) approach is used. The effectiveness of the proposed methodology is shown with a case study.