Dynamic Scheduling Of Photolithography Process Based On Kohonen Neural Network

Semiconductor enterprises should continue to provide more capacity to meet the demands of the highly competitive semiconductor market. Photolithography area is the most reentrant area and its equipment is the most important equipment in semiconductor manufacturing system. To some extend, photolithography area controls the performance of whole semiconductor manufacturing system. This paper researches on the dynamic scheduling in photolithography area.A dynamic scheduling method based on Kohonen neural network is proposed in this paper, to accommodate the complex changes of state in photolithography area. Get the improvement of performance of photolithography area, in order to increase the performance value of the whole semiconductor manufacturing system.The method has been integrated into a dynamic scheduling system, which is composed of sample creating module, learning module, database module, forecast module and controlling module. The learning module learning the samples collected by the sample creating module, to select best scheduling policy according to the state of photolithography area. The controlling module applies suitable scheduling policy to photolithography process based on the forecasted state and the learning results.Finally, the results of simulation experiments indicated that, the proposed method is effective and feasible in realtime scheduling of semiconductor manufacturing system under both closed-loop release policy and open-loop release policy.