Neural network reinforced point defect concentration estimation model for Czochralski-grown silicon crystals

The point defects are the most important and fundamental components of silicon microdefects. Modeling and estimation of their concentration has ever increasing importance. In this work, a simplified model for the vacancy type and self-interstitial-type defects is considered. The problem of the model is explained and a neural network reinforced improvement is adapted to the model. The improved analytical model is compared with the finite volume technique based numerical solution on an application. Finally it is observed that the model gained better accuracy and validity with the aid of a neural network. All simulations are done in MATLAB environment and the results are concluded.