Machine Learning Model Identification on a Single HPGe Crystal Growth Dynamics-Based LSTM Neural Networks

The growth of high-quality single high-purity germanium (HPGe) crystal is of great significance for rare event searches such as dark matter and neutrinoless double beta decay. A Machine learning model identification based on long short-term memory (LSTM) neural network is implemented to the real-time data sets obtained from the crystal growth process using the Czochralski technique. Some Machine learning algorithms such as support vector machine (SVM), decision tree, and random forest are adopted to predict the growth rate and sampling results such as net impurity concentration and mobility. These models are adopted based on their stability and accuracy This study will lead to a crucial breakthrough in understanding the effective segregation coefficient of impurities in HPGe crystal.