Using Artificial Neural Network for Predicting Impurity Concentration in Solid Diffusion Process under Insufficient Input Parameters

An ANN model is proposed to predict the impurity concentration in solid diffusion process when the diffusion coefficient is not known using back-propagation learning technique based on insufficient data for analytical solution. The proposed model was very competitive against the analytical method as the results showed high-performance results with minimal amount of error comparing to the analytical method. Moreover, the proposed ANN model can be used where the analytical methods cannot as in some situations wherethe diffusion coefficient is not available