A collaborative and artificial intelligence approach for semiconductor cost forecasting

• We estimate the cost of a semiconductor product.
• A fuzzy collaborative forecasting system is developed to estimate the semiconductor cost in a collaborative way.
• Both the precision and accuracy of cost estimation are improved.
• Through collaboration, the estimation performance of an existing approach can be enhanced.

Forecasting the unit cost of a semiconductor product is an important task to the manufacturer. However, it is not easy to deal with the uncertainty in the unit cost. In order to effectively forecast the semiconductor unit cost, a collaborative and artificial intelligence approach is proposed in this study. In the proposed methodology, a group of domain experts is formed. These domain experts are asked to configure their own fuzzy neural networks to forecast the semiconductor unit cost based on their viewpoints. A collaboration mechanism is therefore established. To facilitate the collaboration process and to derive a single representative value from these forecasts, a radial basis function (RBF) network is used. The effectiveness of the proposed methodology is shown with a case study.