Research Into the LSTM Neural Network-Based Crystal Growth Process Model Identification

Abstract

In this paper, a model identification method based on a long short-term memory (LSTM) neural network composed of a network structure and training algorithm is used to build a thermal field model that accurately simulates the crystal growth process. The support vector machine (SVM) approach is then adopted to identify model order and lag to determine network input and to improve precision. The thermal field model reflecting the growth process in the Czochralski crystal furnace is simulated. Experimental results and comparative analysis results both suggest that the method proposed by this paper can build an efficient thermal field model which outperforms other methods in terms of precision.