Fuzzy neural network-based rescheduling decision mechanism for semiconductor manufacturing

• The rescheduling framework of SMS is presented as layered scheduling strategies with an optimized decision mechanism.
• The system's states and disturbances including machine failures and rush orders are mathematically characterized.
• An adaptable and robust fuzzy neural networks (FNN) based optimal rescheduling decision mechanism is developed.
• Numerical results demonstrate that the proposed method outperforms in terms of daily movement and machine utilization.

Most semiconductor manufacturing systems (SMS) operate in a highly dynamic and unpredictable environment. The production rescheduling strategy addresses uncertainty and improves SMS performance. The rescheduling framework of SMS is presented as layered scheduling strategies with an optimization rescheduling decision mechanism. A fuzzy neural network (FNN) based rescheduling decision model is implemented which can rapidly choose an optimized rescheduling strategy to schedule the semiconductor wafer fabrication lines according to current system disturbances. The mapping between the input of FNN, such as disturbances, system state parameters, and the output of FNN, optimal rescheduling strategies, is constructed. An example of a semiconductor fabrication line in Shanghai is given. The experimental results demonstrate the effectiveness of proposed FNN-based rescheduling decision mechanism approach over the alternatives such as back-propagation neural network (BPNN) and multivariate regression (MR).