Abstract: A method for growing long-seed DKDP crystal by two-dimensional motion grows the crystal along the cylindrical surface, and there is no cylinder-cone interface with low optical quality, while avoiding three flow regions which are inevitable in the crystal growth process by rotating crystal method, including incident flow, side flow and wake flow, and easily cause inclusion formation. The long seed crystal moves periodically in the fresh solution, four cylindrical surfaces can achieve reversible shear flow in one cycle, and any point on the cylindrical surface experiences the same hydrodynamic conditions in one movement cycle, so that the solute supply is sufficient and uniform, the growth velocity is improved, and the stability of morphology is ensured. The method facilitates rapid growth of high quality DKDP crystals and provides a better solution for the large-size, high-quality DKDP crystal growth required by the ICF laser device.

Abstract: A preparation method of conductive gallium oxide based on deep learning and vertical Bridgman growth method. The prediction method includes: obtaining a preparation data of the conductive gallium oxide single crystal, the preparation data includes a seed crystal data, an environmental data, a control data and a raw material data, and the raw material data includes a doping type data and a conductive doping concentration; preprocessing the preparation data to obtain a preprocessed preparation data; 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 includes a predicted carrier concentration.

Abstract: 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.

Abstract: 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.

Abstract: The present application discloses a preparation method of high resistance gallium oxide based on deep learning and vertical Bridgman growth method. The prediction method comprises: obtaining a preparation data of the high resistance gallium oxide single crystal, the preparation data comprises a seed crystal data, an environmental data, a control data and a raw material data, and the raw material data comprises a doping type data and a doping concentration; preprocessing the preparation data to obtain a preprocessed preparation data; inputting the preprocessed preparation data into a trained neural network model, and obtaining a predicted property data corresponding to the high resistance gallium oxide single crystal through the trained neural network model, the predicted property data comprises a predicted resistivity.

Abstract: 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.

Abstract: 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.

Abstract: A preparation method of high resistance gallium oxide based on deep learning and heat exchange method. The prediction method includes: obtaining a preparation data of the high resistance gallium oxide single crystal, the preparation data including a seed crystal data, an environmental data, a control data, and a raw material data, the control data including a seed crystal coolant flow rate, and the raw material data including a doping type data and a doping concentration; preprocessing the preparation data to obtain a preprocessed preparation data; inputting the preprocessed preparation data into a trained neural network model, and obtaining a predicted property data corresponding to the high resistance gallium oxide single crystal through the trained neural network model, the predicted property data comprises a predicted resistivity. Therefore, the high resistance gallium oxide with a preset resistivity is obtained.

Abstract: A preparation method of conductive gallium oxide based on deep learning and heat exchange method. The prediction method includes: obtaining a preparation data of the conductive gallium oxide single crystal, the preparation data includes 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 includes a doping type data and a conductive doping concentration; preprocessing the preparation data to obtain a preprocessed preparation data; 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 includes a predicted carrier concentration. Therefore, the conductive gallium oxide with a preset carrier concentration is obtained.

Abstract: A method for growing long-seed DKDP crystal by two-dimensional motion grows the crystal along the cylindrical surface, and there is no cylinder-cone interface with low optical quality, while avoiding three flow regions which are inevitable in the crystal growth process by rotating crystal method, including incident flow, side flow and wake flow, and easily cause inclusion formation. The long seed crystal moves periodically in the fresh solution, four cylindrical surfaces can achieve reversible shear flow in one cycle, and any point on the cylindrical surface experiences the same hydrodynamic conditions in one movement cycle, so that the solute supply is sufficient and uniform, the growth velocity is improved, and the stability of morphology is ensured. The method facilitates rapid growth of high quality DKDP crystals and provides a better solution for the large-size, high-quality DKDP crystal growth required by the ICF laser device.

Abstract: A preparation method of conductive gallium oxide based on deep learning and vertical Bridgman growth method. The prediction method includes: obtaining a preparation data of the conductive gallium oxide single crystal, the preparation data includes a seed crystal data, an environmental data, a control data and a raw material data, and the raw material data includes a doping type data and a conductive doping concentration; preprocessing the preparation data to obtain a preprocessed preparation data; 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 includes a predicted carrier concentration.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A pyramidal growth method for long-seed KDP-type crystal. In the growth method provided by the present invention, the lower end of the long-seed crystal is restricted by a lower tray, and the upper end is free to grow into a pyramidal. At the same time, the four prismatic faces at two directions of [100] and [010] can grow, avoiding growth stress problem during crystal growth, and all cut optical elements have high optical quality. Because the growth process is that four prismatic faces with highly similar growth environments grow at the same time and stirring is applied by blade-like stirring paddles during the crystal growth process, the cut optical elements have high optical uniformity.

Abstract: A pyramidal growth method for long-seed KDP-type crystal. In the growth method provided by the present invention, the lower end of the long-seed crystal is restricted by a lower tray, and the upper end is free to grow into a pyramidal. At the same time, the four prismatic faces at two directions of [100] and [010] can grow, avoiding growth stress problem during crystal growth, and all cut optical elements have high optical quality. Because the growth process is that four prismatic faces with highly similar growth environments grow at the same time and stirring is applied by blade-like stirring paddles during the crystal growth process, the cut optical elements have high optical uniformity.

Abstract: Method for limiting growth of KDP-type crystals with a long seed where an upper and a lower ends of the long seed crystal are respectively limited by an upper baffle plate and a lower tray to restrain growth of a pyramidal surface and allow only four prismatic surfaces in [100] and [010] directions to grow. Finally grown crystal contains no pyramid-prism interface that severely restricts quality of optical element, and all cut optical elements have high optical quality. As four prismatic surfaces are subjected to highly similar growing environment and grow simultaneously, all optical elements cut therefrom have high optical uniformity. Due to uniqueness of a cutting angle of a KDP crystal frequency-tripled element, high cutting efficiency is achieved in the element, and an area of a maximum frequency-tripled element that may be cut is known in advance according to a horizontal size of the grown crystal.

Abstract: Method for limiting growth of KDP-type crystals with a long seed where an upper and a lower ends of the long seed crystal are respectively limited by an upper baffle plate and a lower tray to restrain growth of a pyramidal surface and allow only four prismatic surfaces in [100] and [010] directions to grow. Finally grown crystal contains no pyramid-prism interface that severely restricts quality of optical element, and all cut optical elements have high optical quality. As four prismatic surfaces are subjected to highly similar growing environment and grow simultaneously, all optical elements cut therefrom have high optical uniformity. Due to uniqueness of a cutting angle of a KDP crystal frequency-tripled element, high cutting efficiency is achieved in the element, and an area of a maximum frequency-tripled element that may be cut is known in advance according to a horizontal size of the grown crystal.