Improvement of the accuracy of modeling with symbolic regression.

Abstract

We are developing an interference exposure system. This system can expose high fine periodic patterns without shading mask. To improve the operational stability and maintainability of the system, we investigate the enhancement of stage control accuracy and the estimation of anomaly causes by using models. In the investigation, the accuracy of the model is important. In this paper, we report on the improvement of the accuracy of model with the symbolic regression. A genetic algorithm was used to identify a function for the difference between stage and model. We were able to clarify the forces acting on the stage without the motors by interpretation of the obtained function. We thought that the difference between the model and the actual machine contains useful information for know-how and system improvement. We expect that the proposal in this study will provide an opportunity to obtain such knowledge.