Neural-network-based delivery time estimates for prioritized 300-mm automatic material handling operations

This paper deals with lot delivery estimates in a 300-mm automatic material handling system (AMHS), which is composed of several intrabay loops. We adopt a neural network approach to estimate the delivery times for both priority and regular lots. A network model is developed for each intrabay loop. Inputs to the proposed neural network model are the combination of transport requirements, automatic material handling resources, and ratios of priority lots against regular ones. A discrete-event simulation model based on the AMHS in a local 300-mm fab is built. Its outputs are adopted for training the neural network model with the back propagation method. The outputs of the neural network model are the expected delivery times of priority and regular lots in the loop, respectively. For a lot to be transported, its expected delivery time along a potential delivery path is estimated by the summation of all the loop delivery times along the path. A shortest path algorithm is used to find the path with the shortest delivery time among all the possible delivery paths. Numerical experiments based on realistic data from a 300-mm fab indicate that this neural network approach is sound and effective for the prediction of average delivery times. Both the delivery times for priority and regular lots get improved. Specially, for the cases of regular lots, our approach dynamically routes the lots according to the traffic conditions so that the potential blockings in busy loops can be avoided. This neural network approach is applicable to implementing a transport time estimator in dynamic lot dispatching and fab scheduling functions in realizing fully automated 300-mm manufacturing.

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