Real-time prediction of interstitial oxygen concentration in Czochralski silicon using machine learning

Abstract

Abstract We developed a machine learning model to predict interstitial oxygen (Oi) concentration in a Czochralski-grown silicon crystal. A highly accurate prediction can be ensured by selecting the appropriate experimental parameters that represent the change in the furnace conditions. A neural network was trained using the dataset of 450 ingots, and its prediction error for the testing dataset was 4.2 × 1016 atoms cm−3. Finally, a real-time prediction system was developed wherein the crystal growth data are input into the model, and the Oi concentration at the current growth interface is calculated immediately.