Using an artificial neural network prediction model to optimize work-in-process inventory level for wafer fabrication

A proper selection of a work-in-process (WIP) inventory level has great impact onto the productivity of wafer fabrication processes, which can be properly used to trigger the decision of when to release specific wafer lots. However, the selection of an optimal WIP is always a tradeoff amongst the throughput rate, the cycle time and the standard deviation of the cycle time. This study focused on finding an optimal WIP value of wafer fabrication processes by developing an algorithm integrating an artificial neural network (ANN) and the sequential quadratic programming (SQP) method. With this approach, it offered an effective and systematic way to identify an optimal WIP level. Hence, the efficiency of finding the optimal WIP level was greatly improved.

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