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公开(公告)号：US12026616B2
公开(公告)日：2024-07-02
申请号：US17761030
申请日：2021-02-05
发明人： Hongji Qi , Long Zhang , Duanyang Chen
CPC分类号： G06N3/08
摘要： 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.
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公开(公告)号：US20230160098A1
公开(公告)日：2023-05-25
申请号：US17760964
申请日：2021-02-08
发明人： Hongji QI , Duanyang CHEN , Qinglin SAI
摘要： 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.
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3.发明公开公开(公告)号：US20230399768A1
公开(公告)日：2023-12-14
申请号：US18250262
申请日：2021-02-08
发明人： Hongji QI , Duanyang CHEN
CPC分类号： C30B29/10 , C30B11/14 , C30B11/006 , G16C20/30 , G16C20/70
摘要： 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.
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4.发明公开公开(公告)号：US20230170055A1
公开(公告)日：2023-06-01
申请号：US17761322
申请日：2021-02-07
发明人： Hongji QI , Long ZHANG , Duanyang CHEN
IPC分类号： G16C20/10 , G16C20/70 , G06N3/08 , G06N3/0464
CPC分类号： G16C20/10 , G16C20/70 , G06N3/08 , G06N3/0464
摘要： 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.
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公开(公告)号：US20230160096A1
公开(公告)日：2023-05-25
申请号：US17760938
申请日：2021-02-07
发明人： Hongji QI , Duanyang CHEN , Qinglin SAI
摘要： 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.
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公开(公告)号：US12057199B2
公开(公告)日：2024-08-06
申请号：US17761322
申请日：2021-02-07
发明人： Hongji Qi , Long Zhang , Duanyang Chen
IPC分类号： C30B11/02 , G06N3/0464 , G06N3/08 , G16C20/10 , G16C20/70
CPC分类号： G16C20/10 , C30B11/02 , G06N3/0464 , G06N3/08 , G16C20/70
摘要： 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.
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公开(公告)号：US12031230B2
公开(公告)日：2024-07-09
申请号：US17760938
申请日：2021-02-07
发明人： Hongji Qi , Duanyang Chen , Qinglin Sai
摘要： 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.
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公开(公告)号：US12027239B2
公开(公告)日：2024-07-02
申请号：US17760945
申请日：2021-02-08
发明人： Hongji Qi , Duanyang Chen , Qinglin Sai
摘要： 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.
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公开(公告)号：US20230162819A1
公开(公告)日：2023-05-25
申请号：US17760945
申请日：2021-02-08
发明人： Hongji QI , Duanyang CHEN , Qinglin SAI
摘要： 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.
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10.发明公开公开(公告)号：US20230162025A1
公开(公告)日：2023-05-25
申请号：US17761030
申请日：2021-02-05
发明人： Hongji QI , Long ZHANG , Duanyang CHEN
IPC分类号： G06N3/08
CPC分类号： G06N3/08
摘要： 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.
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