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

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.

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► We investigate yield forecasting for a semiconductor product.
► The yield of a semiconductor product is forecasted with a collaborative intelligence approach.
► The maximal consensus and radial basis function is used to aggregate the fuzzy forecasts by the experts.
► With expert collaboration, both the precision and accuracy of the semiconductor yield forecasting can be enhanced.