Modeling the plasma enhanced chemical vapor deposition process using neural networks and genetic algorithms

Silicon dioxide films deposited by plasma-enhanced chemical vapor deposition (PECVD) are useful as interlayer dielectrics for metal-insulator structures. In this study, PECVD modeling using neural networks and genetic algorithms is introduced. The deposition process was characterized via a fractional factorial experiment, and data from this experiment were used to train feed-forward neural networks using the error back-propagation algorithm. The networks were optimized to minimize both learning and prediction error. The optimal neural process models were then used for recipe synthesis to generate the proper deposition conditions to obtain specific film properties. The response surfaces of the neural process models were explored using genetic algorithms, and the performance of this procedure was evaluated by comparing the deposition conditions indicated by the generic algorithms with the neural process model predictions