A short-term capacity trading method for semiconductor fabs with partnership

This paper presents a capacity trading method for two semiconductor fabs that have established a capacity-sharing partnership. A fab that is predicted to have insufficient capacity at some workstations in a short-term period (e.g. one week) could purchase tool capacity from its partner fab. The population of such a capacity-trading portfolio may be quite huge. The proposed method involves three modules. We first use discrete-event simulation to identify the trading population. Secondly, some randomly sampled trading portfolios with their performance measured by simulation are used to develop a neural network, which can efficiently evaluate the performance of a trading portfolio. Thirdly, a genetic algorithm (GA) embedded with the developed neural network is used to find a near-optimal trading portfolio from the huge trading population. Experiment results indicate that the proposed trading method outperforms two other benchmarked methods in terms of number of completed operations, number of wafer outs, and mean cycle time.

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