Applying the hybrid fuzzy c-means-back propagation network approach to forecast the effective cost per die of a semiconductor product

Forecasting the unit cost of every product type in a factory is an important task to the factory. After the unit cost of every product type in a factory is accurately forecasted, several managerial goals (including pricing, cost down projecting, capacity planning, ordering decision support, and guiding subsequent operations) can be simultaneously achieved. However, it is not easy to deal with the uncertainty in the unit cost. In addition, most references in this field were focused on costing and seldom investigated the forecasting of the unit cost. To tackle these problems, the hybrid fuzzy linear regression (FLR) and back propagation network (BPN) approach is applied to forecast the unit cost of every product type in a wafer fabrication plant, which is usually referred to as the determination of the effective cost per die. In practical situations the long-term effective cost per die of a product type is usually approximated with a linear regression (LR) equation, according to the “continuous cost down” philosophy, which is prone to error. Conversely, the proposed FLR–BPN approach is more accurate and be able to deal with the uncertainty in the unit cost in a simple and intuitive way. For evaluating the effectiveness of the proposed methodology, a demonstrative case was used. Experimental results showed that the hybrid FLR–BPN approach was superior to some existing approaches in forecasting accuracy and precision.

► We estimate the unit cost of a DRAM product.
► The actual unit cost and an interval including the actual unit cost are estimated simultaneously.
► Both the precision and accuracy of unit cost forecasting are improved.
► Through collaboration, the forecasting performance of an existing approach can be enhanced.