Process modeling and optimization of PECVD silicon nitride coated on silicon solar cell using neural networks

Abstract

The process of plasma enhanced chemical vapor deposition silicon nitride films coated on silicon solar cells as antireflection layers is modeled and optimized using neural networks. This neural network model is built based on the robust design technique with process input–output experimental data. The input parameters selected are as substrate temperature, SiH 4 and NH 3 flow rates, and RF power; while the output parameters are deposition rate, refractive index, and short circuit current. This model can then be applied to predict the input–output relationships of the process. Optimal operating conditions of this process can be determined using this model.