Preparation method of conductive gallium oxide based on deep learning and heat exchange method

A prediction method of conductive gallium oxide based on deep learning and heat exchange method, comprising:

• obtaining a preparation data of a conductive gallium oxide single crystal, the preparation data comprises a seed crystal data, an environmental data, a control data, and a raw material data, the control data comprises a seed crystal coolant flow rate, and the raw material data comprises a doping type data and a conductive doping concentration;
• preprocessing the preparation data to obtain a preprocessed preparation data;
• and
• inputting the preprocessed preparation data into a trained neural network model, and obtaining a predicted property data corresponding to the conductive gallium oxide single crystal through the trained neural network model, the predicted property data comprises a predicted carrier concentration.

A preparation method of conductive gallium oxide based on deep learning and heat exchange method, comprising:

• obtaining a target property data of a target conductive gallium oxide single crystal, the target property data comprises a target carrier concentration;
• determining a target preparation data corresponding to the target conductive gallium oxide single crystal according to the target property data and a trained neural network model, the target preparation data comprises a seed crystal data, an environmental data, a control data, and a raw material data, the control data comprises a seed crystal coolant flow rate, and the raw material data comprises a doping type data and a conductive doping concentration;
• and
• preparing, based on the heat exchange method, the target conductive gallium oxide single crystal according to the target preparation data.