Use of influence diagrams and neural networks in modeling semiconductor manufacturing processes

An adaptive learning architecture for modeling manufacturing processes involving several control variables is described. The use of this architecture to process modeling and recipe synthesis for deposition rate, stress, and film thickness in low-pressure chemical vapor deposition (LPCVD) of undoped polysilicon is discussed. In this architecture the model for a process is generated by combining the qualitative knowledge of human experts, captured in the form of influence diagrams, and the learning abilities of neural networks for extracting the quantitative knowledge that relates the parameters of a process. To evaluate the merits of this methodology, the accuracy of these new models is compared to that of more conventional models generated by the use of first principles and/or statistical regression analysis. The models generated by the integration of influence diagrams and neural networks are shown to have half the error or less, even though given only half as much information in creating the models. Furthermore, it is shown that, by employing the generalization ability of neural networks in the synthesis algorithm, new recipes can be produced for the process. Two such recipes are generated for the LPCVD process. One is a zero-stress polysilicon film recipe; the second is a uniform deposition rate recipe which is based on the use of a nonuniform temperature distribution during deposition