A production method of monocrystalline silicon includes: measuring an emissivity of an inner wall surface of a top chamber; and determining a target resistivity of monocrystalline silicon based on the emissivity measured in the measuring, thereby producing the monocrystalline silicon. In determining the target emissivity on a crystal center axis at a position for starting formation of a straight body of the monocrystalline silicon in the producing, when the emissivity is 0.4 or less, the target resistivity is determined to be less than a resistivity value of 3.0 mΩ.Math.cm when the dopant is arsenic.

A quality prediction method, a preparation method and a system of high resistance gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of high resistance gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the high resistance gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted resistivity.

A quality prediction method, a preparation method and a system of high resistance gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of high resistance gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the high resistance gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted resistivity.

A high resistance gallium oxide quality prediction method based on deep learning and an edge-defined film-fed crystal growth method, a preparation method and a system; the quality prediction method includes the following steps: obtaining preparation data of a high resistance gallium oxide single crystal prepared by the edge-defined film-fed crystal growth method, the preparation data including seed crystal data, environment data and control data, and the control data including doping element concentration and doping element type; preprocessing the preparation data to obtain preprocessed preparation data; inputting the preprocessing preparation data into a trained neural network model, acquiring the predicted quality data corresponding to the high resistance gallium oxide single crystal through the trained neural network model, the predicted quality data including predicted resistivity.

A high resistance gallium oxide quality prediction method based on deep learning and an edge-defined film-fed crystal growth method, a preparation method and a system; the quality prediction method includes the following steps: obtaining preparation data of a high resistance gallium oxide single crystal prepared by the edge-defined film-fed crystal growth method, the preparation data including seed crystal data, environment data and control data, and the control data including doping element concentration and doping element type; preprocessing the preparation data to obtain preprocessed preparation data; inputting the preprocessing preparation data into a trained neural network model, acquiring the predicted quality data corresponding to the high resistance gallium oxide single crystal through the trained neural network model, the predicted quality data including predicted resistivity.

A conductive gallium oxide quality prediction method based on deep learning and an edge-defined film-fed crystal growth method, a preparation method and a system; the quality prediction method includes the following steps: obtaining preparation data of a conductive gallium oxide single crystal prepared by the edge-defined film-fed crystal growth method, the preparation data including seed crystal data, environment data and control data, and the control data including doping element concentration and doping element type; preprocessing the preparation data to obtain preprocessed preparation data; inputting the preprocessing preparation data into a trained neural network model, acquiring the predicted quality data corresponding to the conductive gallium oxide single crystal through the trained neural network model, the predicted quality data including predicted carrier concentration.

A conductive gallium oxide quality prediction method based on deep learning and an edge-defined film-fed crystal growth method, a preparation method and a system; the quality prediction method includes the following steps: obtaining preparation data of a conductive gallium oxide single crystal prepared by the edge-defined film-fed crystal growth method, the preparation data including seed crystal data, environment data and control data, and the control data including doping element concentration and doping element type; preprocessing the preparation data to obtain preprocessed preparation data; inputting the preprocessing preparation data into a trained neural network model, acquiring the predicted quality data corresponding to the conductive gallium oxide single crystal through the trained neural network model, the predicted quality data including predicted carrier concentration.

A quality prediction method, a preparation method and a system of conductive gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of conductive gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the conductive gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted carrier concentration.

A quality prediction method, a preparation method and a system of conductive gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of conductive gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the conductive gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted carrier concentration.

A method for producing Si ingot single crystal including a Si ingot single crystal growing step, a temperature gradient controlling step and a continuous growing step is provided. In the growing step, the Si ingot single crystal is grown in silicon melt in crucible, and the growing step includes providing a low-temperature region in the Si melt and providing a silicon seed to contact the melt surface of the silicon melt to start crystal growth, and silicon single crystal grows along the melt surface of the silicon melt and toward the inside of the silicon melt. In the temperature gradient controlling step, the under-surface temperature gradient of the silicon single crystal is G1, the above-surface temperature gradient of the silicon single crystal is G2, G1 and G2 satisfy: G2/G1<6. The step of controlling the temperature gradient of silicon single crystal is repeated to obtain the Si ingot single crystal.