WO2022141766A1 - 一种基于深度学习和热交换法的导电型氧化镓制备方法 - Google Patents

一种基于深度学习和热交换法的导电型氧化镓制备方法 Download PDF

Info

Publication number
WO2022141766A1
WO2022141766A1 PCT/CN2021/076073 CN2021076073W WO2022141766A1 WO 2022141766 A1 WO2022141766 A1 WO 2022141766A1 CN 2021076073 W CN2021076073 W CN 2021076073W WO 2022141766 A1 WO2022141766 A1 WO 2022141766A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
preparation
gallium oxide
neural network
network model
Prior art date
Application number
PCT/CN2021/076073
Other languages
English (en)
French (fr)
Inventor
齐红基
陈端阳
Original Assignee
杭州富加镓业科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 杭州富加镓业科技有限公司 filed Critical 杭州富加镓业科技有限公司
Priority to US18/250,262 priority Critical patent/US20230399768A1/en
Priority to JP2023526453A priority patent/JP2023547495A/ja
Priority to EP21912538.2A priority patent/EP4206990A4/en
Publication of WO2022141766A1 publication Critical patent/WO2022141766A1/zh

Links

Images

Classifications

    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
    • C30B29/10—Inorganic compounds or compositions
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B11/00—Single-crystal growth by normal freezing or freezing under temperature gradient, e.g. Bridgman-Stockbarger method
    • C30B11/006—Controlling or regulating
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B11/00—Single-crystal growth by normal freezing or freezing under temperature gradient, e.g. Bridgman-Stockbarger method
    • C30B11/14—Single-crystal growth by normal freezing or freezing under temperature gradient, e.g. Bridgman-Stockbarger method characterised by the seed, e.g. its crystallographic orientation
    • C—CHEMISTRY; METALLURGY
    • C30—CRYSTAL GROWTH
    • C30B—SINGLE-CRYSTAL GROWTH; UNIDIRECTIONAL SOLIDIFICATION OF EUTECTIC MATERIAL OR UNIDIRECTIONAL DEMIXING OF EUTECTOID MATERIAL; REFINING BY ZONE-MELTING OF MATERIAL; PRODUCTION OF A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; SINGLE CRYSTALS OR HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; AFTER-TREATMENT OF SINGLE CRYSTALS OR A HOMOGENEOUS POLYCRYSTALLINE MATERIAL WITH DEFINED STRUCTURE; APPARATUS THEREFOR
    • C30B29/00—Single crystals or homogeneous polycrystalline material with defined structure characterised by the material or by their shape
    • C30B29/10—Inorganic compounds or compositions
    • C30B29/16—Oxides
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
    • G16C20/70—Machine learning, data mining or chemometrics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
    • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation

Definitions

  • the present application relates to the technical field of gallium oxide preparation, in particular to a method for preparing conductive type gallium oxide based on deep learning and heat exchange method.
  • Gallium oxide (Ga 2 O 3 ) single crystal is a transparent semiconductor oxide, which belongs to the wide band gap semiconductor material.
  • ⁇ -phase gallium oxide ( ⁇ -Ga 2 O 3 ) is relatively stable, and ⁇ -Ga 2 O 3 has many advantages, such as large forbidden band width, fast saturation electron drift speed, high thermal conductivity, high breakdown field strength, and stable chemical properties.
  • the high band gap width makes it have high breakdown voltage, coupled with its high saturation electron drift velocity , high thermal conductivity and chemical stability, etc. Broad application prospects.
  • the heat exchange method is one of the methods for preparing gallium oxide. In the prior art, when the conductive type gallium oxide is prepared by the heat exchange method, the conductive type gallium oxide with a predetermined carrier concentration cannot be obtained.
  • the technical problem to be solved by the present invention is to provide a preparation method of conductive gallium oxide based on deep learning and heat exchange method, so as to predict the conductive gallium oxide with a predetermined carrier concentration.
  • the embodiment of the present invention provides a method for predicting conductive type gallium oxide based on deep learning and heat exchange method, including:
  • the preparation data includes: seed crystal data, environmental data, control data and raw material data;
  • the control data includes: seed crystal cooling medium flow, and the raw material data includes: Doping type data and conductive doping concentration;
  • the described conductive gallium oxide prediction method based on deep learning and heat exchange method the described preparation data is preprocessed to obtain preprocessed preparation data, including:
  • pre-processed preparation data is obtained; wherein, the pre-processed preparation data is obtained from the seed crystal data, the environmental data , the control data and the matrix formed by the raw material data.
  • the seed crystal data includes: seed crystal diffraction peak full width at half maximum, seed crystal diffraction peak full width at half maximum deviation value, and seed crystal diameter;
  • the environmental data includes: thermal resistance value of the thermal insulation layer, deviation value of thermal resistance value of the thermal insulation layer, and shape factor of the thermal insulation layer;
  • the control data also includes: coil input power and coil cooling power.
  • the preprocessed preparation data is obtained according to the seed crystal data, the environmental data, the control data and the raw material data, including :
  • a preparation vector is determined; wherein, the first element in the preparation vector is the one of the deviation value of the half width of the crystal diffraction peak and the diameter of the seed crystal, and the second element in the preparation vector is one of the thermal resistance value of the insulation layer, the deviation value of the thermal resistance value of the insulation layer, and the shape factor of the insulation layer , the third element in the preparation vector is one of the coil input power, the coil cooling power and the flow rate of the seed cooling medium; the fourth element in the preparation vector is the doping type data and all one of the conductive doping concentrations;
  • the preprocessed preparation data is determined.
  • the predicted property data includes: predicted crack data, predicted miscellaneous crystal data, predicted diffraction peak full width at half maximum, and predicted diffraction peak full width at half maximum radial deviation value, predicted diffraction peak width at half maximum axial deviation value, predicted carrier concentration, predicted radial deviation value of carrier concentration, and predicted axial deviation value of carrier concentration.
  • a preparation method of conductive gallium oxide based on deep learning and heat exchange method comprises:
  • Target property data of the target conductivity type gallium oxide single crystal includes: target carrier concentration;
  • the target preparation data corresponding to the target conductivity type gallium oxide single crystal is determined; wherein, the target preparation data includes: seed crystal data, environmental data, control data and raw materials data; the control data includes: seed crystal cooling medium flow rate, and the raw material data includes: doping type data and conductive doping concentration;
  • the target conductivity type gallium oxide single crystal is prepared according to the target preparation data.
  • the target preparation data corresponding to the target conductive gallium oxide single crystal is determined according to the target property data and the trained neural network model, include:
  • the preset preparation data is corrected to obtain target preparation data corresponding to the target conductivity type gallium oxide single crystal.
  • the described method for preparing conductive gallium oxide based on deep learning and heat exchange method wherein, the trained neural network model is obtained by training the following steps:
  • the training data includes: seed crystal training data, environmental training data, control training data and raw material training data;
  • the control training data includes: seed crystal cooling medium flow training data, and the raw material training data includes: doping type data and conductive doping concentration;
  • the prediction generation property data includes: prediction generation carrier concentration
  • the model parameters of the preset neural network model are adjusted and corrected according to the predicted generated property data and the actual property data, so as to obtain a trained neural network model.
  • the preset neural network model includes: a feature extraction module and a fully connected module,
  • the preprocessed training data is input into the feature extraction module, and the feature vector corresponding to the preprocessed training data is obtained by the feature extraction module;
  • the feature vector is input into the fully connected module, and the prediction generation property data obtained from the preprocessed training data is obtained through the fully connected module.
  • a conductive type gallium oxide preparation system based on deep learning and heat exchange method comprising a memory and a processor, wherein the memory stores a computer program, wherein the processor implements any of the above when executing the computer program The steps of the prediction method, or the steps of the preparation method described in any one of the above.
  • the embodiment of the present invention has the following advantages:
  • preprocess the preparation data to obtain preprocessed preparation data
  • input the preprocessed preparation data into the trained neural network model
  • obtain the conductive type gallium oxide single crystal Corresponding to the predicted property data corresponding to the conductive gallium oxide single crystal, the performance of the conductive gallium oxide single crystal can be predicted through the trained neural network model. Therefore, the predetermined carrier concentration can be obtained by adjusting the preparation data. conductivity type gallium oxide.
  • FIG. 1 is a flowchart of a method for predicting conductive gallium oxide based on deep learning and heat exchange method in an embodiment of the present invention
  • FIG. 2 is a schematic structural diagram of a crystal growth furnace in an embodiment of the present invention.
  • FIG. 3 is a schematic diagram of the position and temperature in the furnace of the crystal in the embodiment of the present invention.
  • FIG. 4 is an internal structure diagram of a conductive-type gallium oxide preparation system based on deep learning and heat exchange method in an embodiment of the present invention.
  • the heat exchange method uses the heat exchanger to take away the heat, so that a longitudinal temperature gradient between the cold and the upper heat is formed in the crystal growth area.
  • the temperature gradient is controlled by controlling the gas flow in the heat exchanger and changing the heating input power and cooling power.
  • the melt in the crucible gradually solidifies from the bottom to the top to form a crystal, as shown in FIG. 2 .
  • the prediction method may include the following steps, for example:
  • the preparation data refers to the data of preparation of the conductive type gallium oxide single crystal.
  • the preparation data can be the preparation data configured according to the needs. For example, when it is necessary to predict the performance of the obtained conductive type gallium oxide single crystal under a certain preparation data, it is only necessary to determine the preparation data. data, and preprocess the preparation data to obtain the preprocessed preparation data, and then input the preprocessed preparation data into the trained neural network model, and obtain the prediction property data through the trained neural network model, that is to say, no need After conducting experiments and determining the preparation data, the property data of the conductive type gallium oxide single crystal can be predicted.
  • Preparation data includes: seed crystal data, environmental data, control data and raw material data.
  • the seed crystal data refers to the data of the seed crystal used in the preparation of the conductive gallium oxide single crystal
  • the environmental data refers to the data of the environment in which the crystal is located during the preparation of the conductive gallium oxide single crystal
  • the control data refers to the preparation of the conductive gallium oxide single crystal. Data on control of crystal growth in the process of type gallium oxide single crystal.
  • the raw material data refers to the data of the raw materials used in the process of preparing the conductive gallium oxide single crystal.
  • the conductive doping concentration refers to the concentration of the conductive doping substances in the gallium oxide.
  • the conductive doping substances include: Si, Ge, Sn, Zr, Hf, In, Ta, Nb, V, W, Mo, etc.
  • the doping type data refers to the type data of the doping substance.
  • the flow rate of the seed crystal cooling medium refers to the flow rate of the gas cooling the vicinity of the seed crystal at the bottom of the crucible.
  • the preparation data is preprocessed to obtain the preprocessed preparation data, so that the preprocessed preparation data can be input into the trained neural network model, so that the trained neural network model can analyze the data.
  • the preprocessed preparation data is processed.
  • step S200 preprocessing the preparation data to obtain preprocessed preparation data, including:
  • the preparation data is preprocessed first to obtain the preprocessed preparation data.
  • the preparation data is preprocessed first to obtain the preprocessed preparation data.
  • the seed crystal data includes: the full width at half maximum of the seed crystal diffraction peak, the deviation value of the half width of the seed crystal diffraction peak, and the diameter of the seed crystal;
  • the environmental data includes: the thermal insulation layer heat The resistance value, the thermal resistance value deviation value of the insulation layer, and the shape factor of the insulation layer;
  • the control data also includes: the input power of the coil and the cooling power of the coil.
  • the X-ray diffractometer can be used to test the seed crystal diffraction peak width at half maximum, and the seed crystal diffraction peak half width deviation value includes: the seed crystal diffraction peak half height width radial deviation value and the seed crystal diffraction peak half width Height and width axial deviation value.
  • the radial direction is the direction on the horizontal plane
  • the axial direction is the direction perpendicular to the horizontal plane, that is, the axis of the vertical direction.
  • the radial deviation value of the half height width of the seed crystal diffraction peak can be measured by testing the half height width of the seed crystal diffraction peak on both sides of the seed crystal radial direction, and the difference between the half height width of the seed crystal diffraction peak on both sides of the seed crystal radial direction can be obtained.
  • value, the radial deviation value of the half-width of the seed crystal diffraction peak can be obtained.
  • the axial deviation of the FWHM of the seed crystal diffraction peak can be measured by testing the FWHM of the seed crystal diffraction peak on both sides of the seed crystal axis, and the difference between the FWHM of the seed crystal diffraction peak on both sides of the seed crystal axis can be obtained.
  • value, the axial deviation value of the half-width of the seed crystal diffraction peak can be obtained.
  • the cooling gas is usually blown from bottom to top, and the temperature above the crucible is higher than the temperature below the crucible. Passing upward, the gallium oxide melt in the crucible gradually grows into a gallium oxide single crystal.
  • the bottom of the crucible 1 is narrowed to form a tip, and the seed crystal is located at the tip. That is to say, during the crystal growth process, due to the continuous blowing of cooling gas from below the crucible, the temperature of the crucible gradually decreases from bottom to top, and the melt 3 flows from the crucible 1.
  • the seed crystal at the bottom starts to grow and gradually grows into crystal 2.
  • the seed crystal can be placed on the tip of the crucible 1 after the gallium oxide in the crucible 1 is completely melted.
  • the tip is connected with a cooling medium transmission pipe, and the cooling medium is transmitted through the cooling medium transmission pipe.
  • the seed crystal cooling medium includes water, gas, and oil.
  • gas is used as the seed crystal cooling medium.
  • a thermal insulation layer is provided outside the induction coil 4 , and the thermal insulation layer is used to maintain the temperature.
  • the thermal resistance value of the insulation layer refers to the temperature difference between the two ends of the insulation layer when a unit of heat passes through the insulation layer per unit time. The larger the thermal resistance value of the thermal insulation layer, the stronger the ability of the thermal insulation layer to resist heat transfer, and the better the thermal insulation effect of the thermal insulation layer.
  • the thermal resistance value deviation value of the thermal insulation layer includes: the thermal insulation layer radial thermal resistance value deviation value and the thermal insulation layer axial thermal resistance value deviation value.
  • the deviation value of the radial thermal resistance value of the thermal insulation layer can be obtained by testing the thermal resistance value of the thermal insulation layer on both radial sides of the thermal insulation layer, and obtaining the difference between the thermal resistance values of the thermal insulation layer on the radial two sides of the thermal insulation layer.
  • Deviation value of the radial thermal resistance value of the layer can be obtained by testing the thermal resistance value of the thermal insulation layer on both axial sides of the thermal insulation layer, and obtaining the difference between the thermal resistance values of the thermal insulation layer on the two axial sides of the thermal insulation layer.
  • the deviation value of the axial thermal resistance value of the layer can be obtained by testing the thermal resistance value of the thermal insulation layer on both axial sides of the thermal insulation layer, and obtaining the difference between the thermal resistance values of the thermal insulation layer on the two axial sides of the thermal insulation layer.
  • the shape factor of the insulation layer refers to the value of the shape and size of the insulation area.
  • the shape factor of the insulation layer includes: the diameter of the insulation layer and the height of the insulation layer.
  • the shape factors of the insulation layer include: the length of the insulation layer, the width of the insulation layer, and the height of the insulation layer.
  • step S210 obtaining pre-processed preparation data according to the seed crystal data, the environmental data and the control data, including:
  • the first element in the preparation vector is the full width at half maximum of the seed crystal diffraction peak, the seed crystal diffraction peak
  • the second element in the preparation vector is the thermal resistance value of the thermal insulation layer, the thermal resistance value deviation value of the thermal insulation layer, and the shape factor of the thermal insulation layer.
  • the third element in the preparation vector is one of the coil input power, the coil cooling power and the flow rate of the seed cooling medium; the fourth element in the preparation vector is the doping type data and one of the conductive doping concentrations.
  • the preparation vector (A, B, C, D) is determined.
  • the seed crystal data A is selected from: a seed crystal diffraction peak full width at half maximum A1, a seed crystal diffraction peak full width at half maximum deviation value A2, and a seed crystal diameter A3.
  • the environmental data B is selected from: the thermal resistance value of the thermal insulation layer B1, the thermal resistance value deviation value of the thermal insulation layer B2, and the shape factor of the thermal insulation layer B3.
  • the control data C is selected from: coil input power C1, coil cooling power C2, and seed crystal cooling medium flow rate C3.
  • the raw material data D is selected from: doping type data D1 and conductive doping concentration D2.
  • the predicted property data further includes: predicted crack data, predicted miscellaneous crystal data, predicted diffraction peak full width at half maximum, predicted diffraction peak full width at half maximum radial deviation value, predicted diffraction peak half full width axial deviation value, predicted carrier Concentration radial deviation value and predicted carrier concentration axial deviation value.
  • Crack data refers to crack grade data
  • predicted crack data refers to predicted crack grade data.
  • cracks can be divided into multiple grades. For example, if the cracks are divided into three grades, the crack data are: 1, 2, and 3. .
  • miscellaneous crystal data refers to the miscellaneous crystal grade data
  • predicted miscellaneous crystal data refers to the predicted miscellaneous crystal grade data.
  • miscellaneous crystals can be divided into multiple grades. are: 1, 2, and 3.
  • the predicted diffraction peak FWHM refers to the predicted diffraction peak FWHM
  • the predicted diffraction peak FWHM radial deviation refers to the predicted difference between the diffraction peak FWHM on both sides of the radial direction
  • the predicted diffraction peak FWHM axis The deviation value refers to the predicted difference in the half-width of the diffraction peak on both sides of the axial direction.
  • the prediction property data is obtained through the neural network model.
  • the predicted property data may be one or more, for example, only the predicted crack data is required.
  • the trained neural network model is obtained by training the following steps:
  • the seed crystal training data includes: seed crystal diffraction peak FWHM training data, seed crystal diffraction peak FWHM deviation value training data, and seed crystal diameter training data;
  • the environmental training data includes: thermal insulation layer thermal resistance training data , the training data of thermal resistance value deviation of the insulation layer, and the training data of the shape factor of the insulation layer;
  • the control training data includes: the flow rate of the seed crystal cooling medium, and of course, the control training data also includes: the coil input power training data, the coil cooling power training data.
  • the raw material training data includes: doping type training data and conductive doping concentration training data; and the actual property data includes: actual carrier concentration.
  • the actual property data may also include: actual crack data, actual miscellaneous crystal data, actual diffraction peak FWHM, actual diffraction peak FWHM radial deviation value and actual diffraction peak FWHM axial deviation value, actual diffraction peak FWHM The radial deviation value of the carrier concentration and the axial deviation value of the actual carrier concentration.
  • a training set may also be formed by training data and actual nature data, and a preset neural network model may be trained based on the training set to obtain a trained neural network model.
  • the conductive gallium oxide single crystal was prepared by the heat exchange method, and the data of preparing the conductive gallium oxide single crystal was recorded as the training data.
  • the properties of the gallium single crystal were analyzed to obtain the actual property data. In order to facilitate the training of the neural network model, as much data as possible can be collected to form a training set.
  • A200 Preprocess the training data to obtain preprocessed training data.
  • the training data is preprocessed to obtain the preprocessed training data.
  • the preprocessing process can refer to step S200.
  • the prediction generation property data includes: The predicted generation carrier concentration.
  • the preprocessed training data is input into a preset neural network model, and prediction generation property data is obtained through the preset neural network model.
  • the predicted generation property data further includes: predicted generation of crack data, predicted generation of miscellaneous crystal data, predicted generation of diffraction peak width at half maximum, predicted generation of diffraction peak width at half maximum radial deviation value, predicted generation of diffraction peak width at half maximum axial deviation value, predicted generation carrier concentration radial deviation value, and predicted generated carrier concentration axial deviation value.
  • A400 Adjust the model parameters of the preset neural network model according to the predicted property data and the actual property data, so as to obtain a trained neural network model.
  • the model parameters of the preset neural network model are modified, and the preprocessed training data is input into the preset neural network model.
  • the step of obtaining prediction generation property data corresponding to the preprocessed training data through the preset neural network model ie, step A300 ), until the preset training conditions are met, and a trained neural network model is obtained.
  • the model parameters of the preset neural network model are modified, and the preprocessed training data is input into the preset neural network model.
  • a loss function value of a preset neural network model is determined according to the predicted generated property data and the actual property data, and the preset neural network model is determined according to the loss function value.
  • the model parameters of the network model are corrected.
  • a gradient-based method is used to modify the parameters of the preset neural network model, and after determining the loss function value of the preset neural network model, the preset neural network model is determined according to the loss function value.
  • the gradient of the parameters of the network model, the parameters of the preset neural network model, and the preset learning rate determine the modified parameters of the preset neural network model.
  • the preset training conditions include: the loss function value satisfies the first preset requirement and/or the preset number of times of training the neural network model reaches the first preset number of times.
  • the first preset requirement is determined according to the accuracy and efficiency of the preset neural network model, for example, the loss function value of the preset neural network model reaches a minimum value or does not change any more.
  • the first preset number of times is the preset maximum number of training times of the neural network model, for example, 4000 times.
  • the loss function of the preset neural network model includes: mean square error, root mean square error, mean absolute error, and the like.
  • the preset neural network model includes: a feature extraction module and a fully connected module.
  • the preset neural network model includes: a first convolution unit, a second convolution unit, a third convolution unit, a fourth convolution unit, and a fully connected unit.
  • the first convolution unit includes: two convolution layers and one pooling layer.
  • the second convolution unit, the third convolution unit, and the fourth convolution unit each include three convolution layers and one pooling layer.
  • the fully connected unit includes three fully connected layers.
  • the convolutional layer and the fully connected layer are responsible for mapping and transforming the input data. This process will use parameters such as weights and biases, and also need to use an activation function.
  • the pooling layer is a fixed function operation. Specifically, the convolutional layer plays the role of extracting features; the pooling layer performs a pooling operation on the input features to change their spatial size; and the fully connected layer connects all the data in the previous layer.
  • Step A300 inputting the preprocessed training data into a preset neural network model, and obtaining prediction generation property data corresponding to the preprocessed training data through the preset neural network model, including:
  • A310 Input the preprocessed training data into the feature extraction module, and obtain a feature vector corresponding to the preprocessed training data through the feature extraction module;
  • A320 Input the feature vector into the fully connected module, and obtain the prediction generation property data obtained from the preprocessed training data through the fully connected module.
  • the preprocessed training data is input into a preset neural network model
  • the feature vector corresponding to the preprocessed training data is output through the feature extraction module in the preset neural network model
  • the The feature vector is input to the fully connected module in the pre-training model to obtain prediction generation property data corresponding to the pre-processed training data output by the fully connected module.
  • the preprocessed training data is input into a preset neural network model, and the feature vector corresponding to the preprocessed training data is output through the feature extraction module in the preset neural network model, and the The feature vector is input to the fully connected module in the pre-training model to obtain prediction generation property data corresponding to the pre-processed training data output by the fully connected module.
  • this embodiment provides a method for preparing conductive type gallium oxide based on deep learning and heat exchange method, and the preparation method includes:
  • the target property data includes: a target carrier concentration.
  • the target property data of the target conductivity type gallium oxide single crystal can be determined first, that is, the property data of the desired conductivity type gallium oxide single crystal can be determined.
  • the target property data of the target conductivity type gallium oxide single crystal may also be determined first, that is, the property data of the desired conductivity type gallium oxide single crystal may be determined.
  • the target property data further includes: target crack data, target miscellaneous crystal data, target diffraction peak FWHM, target diffraction peak FWHM radial deviation value, target diffraction peak FWHM axial deviation value, target carrier Concentration radial deviation value and target carrier concentration axial deviation value.
  • B200 Determine target preparation data corresponding to the target conductivity type gallium oxide single crystal according to the target property data and the trained neural network model; wherein the target preparation data includes: seed crystal data, environmental data, and control data and raw material data; the raw material data includes: doping type data and conductive doping concentration.
  • the target preparation data corresponding to the target conductivity type gallium oxide single crystal is determined.
  • the target preparation data corresponding to the target conductivity type gallium oxide single crystal may also be determined according to the target property data and the trained neural network model. It should be noted that since different preparation data can obtain the same property data, when determining the target preparation data corresponding to the target conductivity type gallium oxide single crystal according to the target property data and the trained neural network model , the target preparation data is not unique.
  • One target preparation data is determined according to the control difficulty of each data in the multiple target preparation data, so as to facilitate obtaining the target conductivity type gallium oxide single crystal.
  • B200 according to the target property data and the trained neural network model, determine the target preparation data corresponding to the target conductivity type gallium oxide single crystal, including:
  • B210 Acquire preset preparation data, and perform preprocessing on the preset preparation data to obtain preprocessed preset preparation data.
  • the preparation data may be preset first, and the preset preparation data may be preprocessed to obtain the preprocessed preset preparation data.
  • the preset preparation data may be preprocessed to obtain the preprocessed preset preparation data.
  • step S200 Input the preset preparation data into the trained neural network model to obtain predictive property data, and then modify the preset preparation data according to the predictive property data and the target property data.
  • the preset preparation data can be used as the target preparation data.
  • automatic correction can be performed, or manual correction can be performed.
  • the loss function value can also be determined according to the predicted property data and the target property data, and then the preset preparation data can be corrected according to the loss function value.
  • the preset preparation data is used as target preparation data.
  • the preset correction conditions include: the loss function value satisfies the second preset requirement and/or the number of corrections of the preset preparation data reaches the second preset number of times.
  • the preset preparation data includes: preset seed crystal data, preset environment data, preset control data and preset raw material data;
  • the preset seed crystal data includes: preset seed crystal diffraction peak half width , preset seed crystal diffraction peak half width deviation value and preset seed crystal diameter;
  • the preset environmental data includes: preset thermal resistance value of thermal insulation layer, preset thermal resistance value deviation value of thermal insulation layer, preset thermal insulation layer shape factor;
  • the preset control data includes: preset coil input power, preset coil cooling power, and preset seed crystal cooling medium flow rate.
  • the preset raw material data includes: preset doping type data and preset conductive doping concentration.
  • the target conductivity type gallium oxide single crystal is prepared according to the target preparation data.
  • the target conductivity type gallium oxide single crystal can be prepared according to the target preparation data based on the heat exchange method.
  • the present invention provides a conductive gallium oxide preparation system based on deep learning and heat exchange method, the system can be computer equipment, and the internal structure is shown in FIG. 4 .
  • the system includes a processor, memory, a network interface, a display screen, and an input device connected by a system bus.
  • the processor of the system is used to provide computing and control capabilities.
  • the memory of the system includes a non-volatile storage medium and an internal memory.
  • the nonvolatile storage medium stores an operating system and a computer program.
  • the internal memory provides an environment for the execution of the operating system and computer programs in the non-volatile storage medium.
  • the network interface of the system is used to communicate with external terminals through a network connection.
  • the computer program is executed by the processor to implement a method for predicting gallium oxide based on deep learning and heat exchange method or a method for preparing gallium oxide based on deep learning and heat exchange method.
  • the display screen of the system can be a liquid crystal display screen or an electronic ink display screen
  • the input device of the system can be a touch layer covered on the display screen, or a button, a trackball or a touchpad set on the system shell, or An external keyboard, trackpad, or mouse, etc.
  • FIG. 4 is only a partial structure related to the solution of the present application, and does not constitute a limitation on the system to which the solution of the present application is applied. show more or fewer components, or combine certain components, or have a different arrangement of components.
  • a system for preparing conductive gallium oxide based on deep learning and heat exchange method including a memory and a processor, wherein the memory stores a computer program, and the processor implements the computer program when executing the computer program The steps of the prediction method, or the steps of the preparation method.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Crystallography & Structural Chemistry (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Materials Engineering (AREA)
  • Organic Chemistry (AREA)
  • Metallurgy (AREA)
  • Artificial Intelligence (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Data Mining & Analysis (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Computational Linguistics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Mathematical Physics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Inorganic Chemistry (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Crystals, And After-Treatments Of Crystals (AREA)

Abstract

本申请涉及基于深度学习和热交换法的导电型氧化镓制备方法，预测方法包括：获取导电型氧化镓单晶的制备数据；制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；控制数据包括：籽晶冷却介质流量，原料数据包括：掺杂类型数据及导电掺杂浓度；对制备数据进行预处理；将预处理的制备数据输入训练好的神经网络模型，得到导电型氧化镓单晶对应的预测性质数据；所述预测性质数据包括：预测载流子浓度。先将制备数据进行预处理，得到预处理的制备数据，将预处理的制备数据输入训练好的神经网络模型，得到导电型氧化镓单晶对应的预测性质数据，通过调整制备数据，可以得到预定载流子浓度的导电型氧化镓。

Description

一种基于深度学习和热交换法的导电型氧化镓制备方法 技术领域
本申请涉及氧化镓制备技术领域，特别是涉及一种基于深度学习和热交换法的导电型氧化镓制备方法。
背景技术
氧化镓(Ga 2O 3)单晶是一种透明半导体氧化物，属于宽禁带半导体材料。通常β相氧化镓(β-Ga 2O 3)较稳定，β-Ga 2O 3具有禁带宽度大、饱和电子漂移速度快、热导率高、击穿场强高、化学性质稳定等诸多优点，高的带隙宽度使得其具有高的击穿电压，再加上其高的饱和电子漂移速度、热导率大和化学性质稳定等特性使得β-Ga 2O 3单晶在电子器件领域有着广泛的应用前景。热交换法是制备氧化镓的方法之一，现有技术，采用热交换法制备导电型氧化镓时，无法得到预定载流子浓度的导电型氧化镓。
因此，现有技术有待改进。
发明内容
本发明所要解决的技术问题是，提供一种基于深度学习和热交换法的导电型氧化镓制备方法，以预测得到预定载流子浓度的导电型氧化镓。
本发明实施例提供了一种基于深度学习和热交换法的导电型氧化镓预测方法，包括：
获取导电型氧化镓单晶的制备数据；其中，所述制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；所述控制数据包括：籽晶冷却介质流量，所述原料数据包括：掺杂类型数据和导电掺杂浓度；
对所述制备数据进行预处理，得到预处理的制备数据；
将所述预处理的制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据；所述预测性质数据包括：预测载流子浓度。
所述的基于深度学习和热交换法的导电型氧化镓预测方法，所述对所述制备 数据进行预处理，得到预处理的制备数据，包括：
根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，得到预处理的制备数据；其中，所述预处理的制备数据为由所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据形成的矩阵。
所述的基于深度学习和热交换法的导电型氧化镓预测方法，所述籽晶数据包括：籽晶衍射峰半高宽、籽晶衍射峰半高宽偏差值以及籽晶直径；
所述环境数据包括：保温层热阻值、保温层热阻值偏差值以及保温层形状因子；
所述控制数据还包括：线圈输入功率以及线圈冷却功率。
所述的基于深度学习和热交换法的导电型氧化镓预测方法，所述根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，得到预处理的制备数据，包括：
根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，确定制备向量；其中，所述制备向量中第一元素为所述籽晶衍射峰半高宽、所述籽晶衍射峰半高宽偏差值以及所述籽晶直径中的一个，所述制备向量中第二元素为所述保温层热阻值、保温层热阻值偏差值以及保温层形状因子中的一个，所述制备向量中第三元素为所述线圈输入功率、所述线圈冷却功率以及所述籽晶冷却介质流量中的一个；所述制备向量中第四元素为所述掺杂类型数据和所述导电掺杂浓度中的一个；
根据所述制备向量，确定所述预处理的制备数据。
所述的基于深度学习和热交换法的导电型氧化镓预测方法，所述预测性质数据包括：预测裂纹数据、预测杂晶数据、预测衍射峰半高宽、预测衍射峰半高宽径向偏差值、预测衍射峰半高宽轴向偏差值、预测载流子浓度、预测载流子浓度径向偏差值以及预测载流子浓度轴向偏差值。
一种基于深度学习和热交换法的导电型氧化镓制备方法，所述制备方法包括：
获取目标导电型氧化镓单晶的目标性质数据；所述目标性质数据包括：目标载流子浓度；
根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据；其中，所述目标制备数据包括：籽晶数据、环境 数据、控制数据以及原料数据；所述控制数据包括：籽晶冷却介质流量，所述原料数据包括：掺杂类型数据和导电掺杂浓度；
基于热交换法，根据所述目标制备数据制备得到目标导电型氧化镓单晶。
所述的基于深度学习和热交换法的导电型氧化镓制备方法，所述根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据，包括：
获取预设制备数据，对所述预设制备数据进行预处理，得到预处理的预设制备数据；
将所述预处理的预设制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据；
根据所述预测性质数据、所述目标性质数据，对所述预设制备数据进行修正，以得到所述目标导电型氧化镓单晶对应的目标制备数据。
所述的基于深度学习和热交换法的导电型氧化镓制备方法，其中，所述训练好的神经网络模型采用如下步骤训练得到：
获取导电型氧化镓单晶的训练数据以及所述训练数据对应的实际性质数据；其中，所述训练数据包括：籽晶训练数据、环境训练数据、控制训练数据以及原料训练数据；所述控制训练数据包括：籽晶冷却介质流量训练数据，所述原料训练数据包括：掺杂类型数据和导电掺杂浓度；
对所述训练数据进行预处理，得到预处理的训练数据；
将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据；所述预测生成性质数据包括：预测生成载流子浓度；
根据所述预测生成性质数据以及所述实际性质数据调整所述预设的神经网络模型的模型参数进行修正，以得到训练好的神经网络模型。
所述的基于深度学习和热交换法的导电型氧化镓制备方法，其中，所述预设的神经网络模型包括：特征提取模块和全连接模块，
所述将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据，包括：
将所述预处理的训练数据输入所述特征提取模块，通过所述特征提取模块得 到所述预处理的训练数据对应的特征向量；
将所述特征向量输入所述全连接模块，通过所述全连接模块得到所述预处理的训练数据得到的预测生成性质数据。
一种基于深度学习和热交换法的导电型氧化镓制备系统，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现上述任一项所述的预测方法的步骤，或上述任一项所述的制备方法的步骤。
与现有技术相比，本发明实施例具有以下优点：
先将制备数据进行预处理，得到预处理的制备数据，然后将所述预处理的制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的所述导电型氧化镓单晶对应的预测性质数据，通过训练好的神经网络模型可以对导电型氧化镓单晶的性能进行预测，因此可以通过调整制备数据，可以得到预定载流子浓度的导电型氧化镓。
附图说明
为了更清楚地说明本发明实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明中记载的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1为本发明实施例中基于深度学习和热交换法的导电型氧化镓预测方法的流程图；
图2为本发明实施例中晶体生长炉的结构示意图；
图3为本发明实施例中晶体的炉内位置与温度示意图；
图4为本发明实施例中基于深度学习和热交换法的导电型氧化镓制备系统的内部结构图。
具体实施方式
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述 的实施例仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
热交换法利用热交换器带走热量，使得晶体生长区内形成一下冷上热纵向温度梯度，通过控制热交换器内气体流量的大小及改变加热输入功率及冷却功率的大小来控制温度梯度，从而达成坩埚内熔体由下慢慢向上凝固形成晶体，如图2所示。
下面结合附图，详细说明本发明的各种非限制性实施方式。
参见图1-图3，示出了本发明实施例中的一种基于深度学习和热交换法的导电型氧化镓预测方法。在本实施例中，所述预测方法例如可以包括以下步骤：
S100、获取导电型氧化镓单晶的制备数据；其中，所述制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；所述控制数据包括：籽晶冷却介质流量，所述原料数据包括：掺杂类型数据和导电掺杂浓度。
具体地，制备数据是指制备导电型氧化镓单晶的数据。获取导电型氧化镓单晶的制备数据，该制备数据可以是根据需要配置的制备数据，例如，需要预测某一制备数据下，得到的导电型氧化镓单晶的性能时，只需要确定该制备数据，并对制备数据进行预处理，得到预处理的制备数据，再将预处理的制备数据输入训练好的神经网络模型，通过训练好的神经网络模型得到预测性质数据，也就是说，不需要进行实验，确定好制备数据后，就可以预测导电型氧化镓单晶的性质数据。
制备数据包括：籽晶数据、环境数据、控制数据以及原料数据。籽晶数据是指制备导电型氧化镓单晶的过程中所采用的籽晶的数据，环境数据是指制备导电型氧化镓单晶的过程中晶体所处环境的数据，控制数据是指制备导电型氧化镓单晶的过程中控制晶体生长的数据。原料数据是指制备导电型氧化镓单晶的过程中所采用原料的数据，导电掺杂浓度是指氧化镓中导电型掺杂物质的浓度，导电型掺杂物质包括：Si、Ge、Sn、Zr、Hf、In、Ta、Nb、V、W、Mo等。掺杂类型数据是指掺杂物质的类型数据。籽晶冷却介质流量是指对坩埚底部籽晶附近进行冷却的气体的流量。
S200、对所述制备数据进行预处理，得到预处理的制备数据。
具体地，在得到制备数据后，先对制备数据进行预处理，得到预处理的制备 数据，从而可以将预处理的制备数据输入训练好的神经网络模型中，以便通过训练好的神经网络模型对预处理的制备数据进行处理。
在本申请实施例的一种实现方式中，步骤S200、对所述制备数据进行预处理，得到预处理的制备数据，包括：
S210、根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，得到预处理的制备数据；其中，所述预处理的制备数据为由所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据形成的矩阵。
具体地，在得到制备数据后，先对制备数据进行预处理，得到预处理的制备数据，由于制备数据中各子数据(如籽晶数据、环境数据、控制数据以及原料数据)之间是会相互影响，但是目前无法明确各子数据之间相互影响的程度有多少，因此，需要对制备数据进行预处理，将制备数据中各子数据重新排列组合，形成预处理的制备数据。
在本申请实施例的一种实现方式中，所述籽晶数据包括：籽晶衍射峰半高宽、籽晶衍射峰半高宽偏差值以及籽晶直径；所述环境数据包括：保温层热阻值、保温层热阻值偏差值以及保温层形状因子；所述控制数据还包括：线圈输入功率以及线圈冷却功率。
具体地，籽晶衍射峰半高宽可以采用X射线衍射仪对籽晶进行测试，籽晶衍射峰半高宽偏差值包括：籽晶衍射峰半高宽径向偏差值和籽晶衍射峰半高宽轴向偏差值。径向为位于水平面上方向，轴向为垂直于水平面的方向，也即竖直方向的轴线。籽晶衍射峰半高宽径向偏差值可以通过对籽晶径向两侧测试籽晶衍射峰半高宽，并求得籽晶径向两侧上籽晶衍射峰半高宽之间的差值，即可得到籽晶衍射峰半高宽径向偏差值。籽晶衍射峰半高宽轴向偏差值可以通过对籽晶轴向两侧测试籽晶衍射峰半高宽，并求得籽晶轴向两侧上籽晶衍射峰半高宽之间的差值，即可得到籽晶衍射峰半高宽轴向偏差值。
由于热交换法制备氧化镓单晶时，通常冷却气体自下向上吹，坩埚上方的温度高于坩埚下方的温度，如图2所示，通过从坩埚下方不断吹冷却气体，使冷量自下向上传递，坩埚中的氧化镓熔体逐渐生长成氧化镓单晶。坩埚1的底部收窄形成尖部，籽晶位于尖部，也就是说，在晶体生长过程中，由于从坩埚下方不断吹冷却气体，坩埚的温度自下向上逐渐降低，熔体3从坩埚1的底部的籽晶处开 始生长，逐渐生长成晶体2。当然，籽晶可以是在坩埚1中的氧化镓完全融化后，放置于坩埚1的尖部。尖部连接有冷却介质传输管，通过冷却介质传输管传输冷却介质，籽晶冷却介质包括水、气、油，优选地，采用气体作为籽晶冷却介质。
如图2所示，在感应线圈4外设置有保温层，保温层用于保持温度。保温层热阻值是指单位时间内有单位热量通过保温层时，保温层两端的温度差。保温层热阻值越大，表明保温层抵抗传热的能力越强，保温层的保温效果越好。
保温层热阻值偏差值包括：保温层径向热阻值偏差值和保温层轴向热阻值偏差值。保温层径向热阻值偏差值可以通过对保温层径向两侧测试保温层热阻值，并求得保温层径向两侧上保温层热阻值之间的差值，即可得到保温层径向热阻值偏差值。保温层轴向热阻值偏差值可以通过对保温层轴向两侧测试保温层热阻值，并求得保温层轴向两侧上保温层热阻值之间的差值，即可得到保温层轴向热阻值偏差值。
保温层形状因子是指保温区形状尺寸的值，例如，采用圆柱形保温层时，则保温层形状因子包括：保温层的直径和保温层的高度。采用立方体形保温层时，则保温层形状因子包括：保温层的长度、保温层的宽度以及保温层的高度。
线圈输入功率是指生长晶体时感应线圈的输入功率，线圈冷却功率是指冷却时对应的功率，由于感应线圈采用中空的感应线圈，在降温时，在感应线圈中通入冷却介质，使得感应线圈形成冷却线圈，通过冷却介质在冷却线圈中不断流动进行冷却。线圈冷却功率可以根据冷却介质的类型和冷却介质的流量确定，冷却介质的类型包括：水、油、气，冷却介质的流量可以根据冷却介质的流速和冷却线圈的直径确定。籽晶冷却水流量是指冷却时冷却水的流量。
在本申请实施例的一种实现方式中，步骤S210、根据所述籽晶数据、所述环境数据以及所述控制数据，得到预处理的制备数据，包括：
S211、根据所述籽晶数据、所述环境数据以及所述控制数据，确定制备向量；其中，所述制备向量中第一元素为所述籽晶衍射峰半高宽、所述籽晶衍射峰半高宽偏差值以及所述籽晶直径中的一个，所述制备向量中第二元素为所述保温层热阻值、所述保温层热阻值偏差值以及所述保温层形状因子中的一个，所述制备向量中第三元素为所述线圈输入功率、所述线圈冷却功率以及所述籽晶冷却介质流量中的一个；所述制备向量中第四元素为所述掺杂类型数据和所述导电掺杂浓度 中的一个。
S212、根据所述制备向量，确定所述预处理的制备数据。
具体地，根据籽晶数据A、环境数据B、控制数据C以及原料数据D，确定制备向量(A，B，C，D)。籽晶数据A选自：籽晶衍射峰半高宽A1、籽晶衍射峰半高宽偏差值A2以及籽晶直径A3。环境数据B选自：保温层热阻值B1、保温层热阻值偏差值B2、保温层形状因子B3。控制数据C选自：线圈输入功率C1、线圈冷却功率C2以及籽晶冷却介质流量C3。原料数据D选自：掺杂类型数据D1、导电掺杂浓度D2。也就是说，制备向量(A，B，C，D)中A可以是A1、A2、A3中的一个，B可以是B1、B2、B3中的一个，C可以是C1、C2、C3中的一个，D可以是D1、D2中的一个。则可以形成54个制备向量。
将所有制备向量按照序号排列形成矩阵，则得到了预处理的制备数据。
具体地，预处理的制备数据如下：
Figure PCTCN2021076073-appb-000001
当然，还采用其他的排列形式，得到预处理的制备数据。
S300、将所述预处理的制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据；所述预测性质数据包括：预测载流子浓度。
所述预测性质数据还包括：预测裂纹数据、预测杂晶数据、预测衍射峰半高宽、预测衍射峰半高宽径向偏差值、预测衍射峰半高宽轴向偏差值、预测载流子浓度径向偏差值以及预测载流子浓度轴向偏差值。
裂纹数据是指裂纹等级数据，预测裂纹数据是指预测的裂纹等级数据，例如，可以将裂纹分为多个等级，举例说明，裂纹分为3级，则裂纹数据分别为：1、2以及3。
杂晶数据是指杂晶等级数据，预测杂晶数据是指预测的杂晶等级数据，例如，可以将杂晶分为多个等级，举例说明，杂晶分为3级，则杂晶数据分别为：1、2以及3。
预测衍射峰半高宽是指预测的衍射峰半高宽，预测衍射峰半高宽径向偏差 值是指在径向两侧衍射峰半高宽的预测差值，预测衍射峰半高宽轴向偏差值是指在轴向两侧衍射峰半高宽的预测差值。
通过将预处理的制备数据输入训练好的神经网络模型中，通过神经网络模型得到预测性质数据。需要说明的是，预测性质数据可以是一个或多个，例如，仅需要预测裂纹数据。
在本申请实施例的一种实现方式中，所述训练好的神经网络模型采用如下步骤训练得到：
A100、获取导电型氧化镓单晶的训练数据以及所述训练数据对应的实际性质数据；其中，所述训练数据包括：籽晶训练数据、环境训练数据、控制训练数据以及原料训练数据；所述原料训练数据包括：掺杂类型数据和导电掺杂浓度。
具体地，训练数据是指制备导电型氧化镓单晶并用于训练的数据，实际性质数据是指制备得到的导电型氧化镓单晶的实际性质的数据。通过训练数据以及实际性质数据形成训练集，基于该训练集训练预设的神经网络模型，得到训练好的神经网络模型。所述控制数据包括：线圈输入功率和线圈冷却功率。所述籽晶训练数据包括：籽晶衍射峰半高宽训练数据、籽晶衍射峰半高宽偏差值训练数据以及籽晶直径训练数据；所述环境训练数据包括：保温层热阻值训练数据、保温层热阻值偏差值训练数据、保温层形状因子训练数据；所述控制训练数据包括：籽晶冷却介质流量，当然，所述控制训练数据还包括：线圈输入功率训练数据、线圈冷却功率训练数据。所述的原料训练数据包括：掺杂类型训练数据、导电掺杂浓度训练数据；所述实际性质数据包括：实际载流子浓度。当然，所述实际性质数据还可以包括：实际裂纹数据、实际杂晶数据、实际衍射峰半高宽、实际衍射峰半高宽径向偏差值以及实际衍射峰半高宽轴向偏差值、实际载流子浓度径向偏差值以及实际载流子浓度轴向偏差值。
当然还可以是通过训练数据以及实际性质数据形成训练集，基于该训练集训练预设的神经网络模型，得到训练好的神经网络模型。
在采集数据得到训练集时，采用热交换法制备导电型氧化镓单晶，并记录制备导电型氧化镓单晶的数据作为训练数据，在得到导电型氧化镓单晶后，，对导电型氧化镓单晶的性质进行分析得到实际性质数据。为了便于神经网络模 型的训练，可以采集尽量多的数据形成训练集。
A200、对所述训练数据进行预处理，得到预处理的训练数据。
具体地，在得到训练数据后，对训练数据进行预处理，得到预处理的训练数据。预处理过程可以参考步骤S200。
A300、将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据；所述预测生成性质数据包括：预测生成载流子浓度。
具体地，将预处理的训练数据输入预设的神经网络模型，通过预设的神经网络模型得到预测生成性质数据。所述预测生成性质数据还包括：预测生成裂纹数据、预测生成杂晶数据、预测生成衍射峰半高宽、预测生成衍射峰半高宽径向偏差值、预测生成衍射峰半高宽轴向偏差值、预测生成载流子浓度径向偏差值以及预测生成载流子浓度轴向偏差值。
A400、根据所述预测生成性质数据以及所述实际性质数据调整所述预设的神经网络模型的模型参数进行修正，以得到训练好的神经网络模型。
具体地，根据所述预测生成性质数据以及所述实际性质数据，对所述预设的神经网络模型的模型参数进行修正，并继续执行将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据的步骤(即步骤A300)，直至满足预设训练条件，得到训练好的神经网络模型。
具体地，根据所述预测生成性质数据以及所述实际性质数据，对所述预设的神经网络模型的模型参数进行修正，并继续执行将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据的步骤，直至满足预设训练条件，得到训练好的神经网络模型。也就是说，若所述预设的神经网络模型满足预设训练条件，则得到训练好的神经网络模型。若所述预设的神经网络模型不满足预设训练条件，则返回步骤A300，直至所述预设的神经网络模型满足预设训练条件，得到训练好的神经网络模型。
在本发明实施例的一种实现方式中，根据所述预测生成性质数据以及所述实际性质数据确定预设的神经网络模型的损失函数值，根据所述损失函数值对 所述预设的神经网络模型的模型参数进行修正。具体地，采用基于梯度的方法对所述预设的神经网络模型的参数进行修正，确定所述预设的神经网络模型的损失函数值后，根据所述损失函数值对所述预设的神经网络模型的参数的梯度、所述预设的神经网络模型的参数以及预设学习率，确定所述预设的神经网络模型的修正的参数。
所述预设训练条件包括：损失函数值满足第一预设要求和/或所述预设的神经网络模型的训练次数达到第一预设次数。
所述第一预设要求根据所述预设的神经网络模型的精度和效率确定，例如，所述预设的神经网络模型的损失函数值达到最小值或者不再变化。所述第一预设次数为所述预设的神经网络模型的最大训练次数，例如，4000次等。
预设的神经网络模型的损失函数包括：均方误差、均方根误差、平均绝对误差等。
在本申请实施例的一种实现方式中，所述预设的神经网络模型包括：特征提取模块和全连接模块。
举例说明，预设的神经网络模型包括：第一卷积单元、第二卷积单元、第三卷积单元、第四卷积单元以及全连接单元。具体地，第一卷积单元包括：两个卷积层和一个池化层。第二卷积单元、第三卷积单元以及第四卷积单元均包括三个卷积层和一个池化层。全连接单元包括三个全连接层。
卷积层和全连接层负责对输入数据进行映射变换，这个过程会用到权值和偏置等参数，也需要使用激活函数。池化层是一个固定不变的函数操作。具体地，卷积层起到提取特征的作用；池化层对输入特征进行池化操作，改变其空间尺寸；而全连接层是对前一次层中所有数据进行全部连接的。
步骤A300、将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据，包括：
A310、将所述预处理的训练数据输入所述特征提取模块，通过所述特征提取模块得到所述预处理的训练数据对应的特征向量；
A320、将所述特征向量输入所述全连接模块，通过所述全连接模块得到所述预处理的训练数据得到的预测生成性质数据。
具体地，将所述预处理的训练数据输入预设的神经网络模型，通过所述预设 的神经网络模型中的所述特征提取模块输出所述预处理的训练数据对应的特征向量，并将所述特征向量输入所述预训练模型中的所述全连接模块，得到所述全连接模块输出的所述预处理的训练数据对应的预测生成性质数据。
还可以是将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型中的所述特征提取模块输出所述预处理的训练数据对应的特征向量，并将所述特征向量输入所述预训练模型中的所述全连接模块，得到所述全连接模块输出的所述预处理的训练数据对应的预测生成性质数据。
基于上述基于深度学习和热交换法的导电型氧化镓预测方法，本实施例提供了一种基于深度学习和热交换法的导电型氧化镓制备方法，所述制备方法包括：
B100、获取目标导电型氧化镓单晶的目标性质数据；所述目标性质数据包括：目标载流子浓度。
具体地，如果需要得到目标导电型氧化镓单晶时，可以先确定目标导电型氧化镓单晶的目标性质数据，也就是说，确定想要得到的导电型氧化镓单晶的性质数据。当然，还可以是先确定目标导电型氧化镓单晶的目标性质数据，也就是说，确定想要得到的导电型氧化镓单晶的性质数据。所述目标性质数据还包括：目标裂纹数据、目标杂晶数据、目标衍射峰半高宽、目标衍射峰半高宽径向偏差值、目标衍射峰半高宽轴向偏差值、目标载流子浓度径向偏差值以及目标载流子浓度轴向偏差值。
B200、根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据；其中，所述目标制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；所述原料数据包括：掺杂类型数据和导电掺杂浓度。
具体地，根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据。当然，也可以是根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据。需要说明的时，由于不同的制备数据可以得到相同的性质数据，因此，在根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据时，目标制备数据并不是唯一的，根据多个目标制备数据中各数据的控制难易程度确定一个目标制备数据，从而便于得到目标导电型氧化 镓单晶。
在本实施例中的一种实现方式中，B200、根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据，包括：
B210、获取预设制备数据，对所述预设制备数据进行预处理，得到预处理的预设制备数据。
B220、将所述预处理的预设制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据。
B230、根据所述预测性质数据、所述目标性质数据，对所述预设制备数据进行修正，以得到所述目标导电型氧化镓单晶对应的目标制备数据。
具体地，可以先预设制备数据，并对预设制备数据进行预处理，得到预处理的预设制备数据，具体预处理过程可以参考步骤S200。将预设制备数据输入训练好的神经网络模型，可以得到预测性质数据，然后根据所述预测性质数据、所述目标性质数据，对所述预设制备数据进行修正，当所述预测性质数据与所述目标性质数据之间的差值小于预设阈值时，则可以将该预设制备数据作为目标制备数据。在对预设制备数据进行修正时，可以进行自动修正，也可以进行人工修正。当然，也可以根据所述预测性质数据与所述目标性质数据，确定损失函数值，然后根据该损失函数值对预设制备数据进行修正，当该损失函数值满足预设修正条件时，可以将该预设制备数据作为目标制备数据。预设修正条件包括：损失函数值满足第二预设要求和/或所述预设制备数据的修正次数达到第二预设次数。
需要说明的是，预设制备数据包括：预设籽晶数据、预设环境数据、预设控制数据以及预设原料数据；所述预设籽晶数据包括：预设籽晶衍射峰半高宽、预设籽晶衍射峰半高宽偏差值以及预设籽晶直径；所述预设环境数据包括：预设保温层热阻值、预设保温层热阻值偏差值、预设保温层形状因子；所述预设控制数据包括：预设线圈输入功率、预设线圈冷却功率以及预设籽晶冷却介质流量。所述预设原料数据包括：预设掺杂类型数据、预设导电掺杂浓度。
B300、基于热交换法，根据所述目标制备数据制备得到目标导电型氧化镓单晶。
具体地，在得到目标制备数据后，则可以基于热交换法，根据所述目标制 备数据制备得到目标导电型氧化镓单晶。
基于上述预测方法或上述制备方法，本发明提供了一种基于深度学习和热交换法的导电型氧化镓制备系统，该系统可以是计算机设备，内部结构如图4所示。该系统包括通过系统总线连接的处理器、存储器、网络接口、显示屏和输入装置。其中，该系统的处理器用于提供计算和控制能力。该系统的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该系统的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现基于深度学习和热交换法的氧化镓的预测方法或基于深度学习和热交换法的氧化镓的制备方法。该系统的显示屏可以是液晶显示屏或者电子墨水显示屏，该系统的输入装置可以是显示屏上覆盖的触摸层，也可以是系统外壳上设置的按键、轨迹球或触控板，还可以是外接的键盘、触控板或鼠标等。
本领域技术人员可以理解，图4所示的仅仅是与本申请方案相关的部分结构的框图，并不构成对本申请方案所应用于其上的系统的限定，具体的系统可以包括比图中所示更多或更少的部件，或者组合某些部件，或者具有不同的部件布置。
在一个实施例中，提供了一种基于深度学习和热交换法的导电型氧化镓制备系统，包括存储器和处理器，所述存储器存储有计算机程序，所述处理器执行所述计算机程序时实现所述的预测方法的步骤，或所述的制备方法的步骤。
以上实施例的各技术特征可以进行任意的组合，为使描述简洁，未对上述实施例中的各个技术特征所有可能的组合都进行描述，然而，只要这些技术特征的组合不存在矛盾，都应当认为是本说明书记载的范围。

Claims (10)

  1. 一种基于深度学习和热交换法的导电型氧化镓预测方法，其特征在于，所述预测方法包括：
    获取导电型氧化镓单晶的制备数据；其中，所述制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；所述控制数据包括：籽晶冷却介质流量，所述原料数据包括：掺杂类型数据和导电掺杂浓度；
    对所述制备数据进行预处理，得到预处理的制备数据；
    将所述预处理的制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据；所述预测性质数据包括：预测载流子浓度。
  2. 根据权利要求1所述的基于深度学习和热交换法的导电型氧化镓预测方法，其特征在于，所述对所述制备数据进行预处理，得到预处理的制备数据，包括：
    根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，得到预处理的制备数据；其中，所述预处理的制备数据为由所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据形成的矩阵。
  3. 根据权利要求2所述的基于深度学习和热交换法的导电型氧化镓预测方法，其特征在于，所述籽晶数据包括：籽晶衍射峰半高宽、籽晶衍射峰半高宽偏差值以及籽晶直径；
    所述环境数据包括：保温层热阻值、保温层热阻值偏差值以及保温层形状因子；
    所述控制数据还包括：线圈输入功率以及线圈冷却功率。
  4. 根据权利要求3所述的基于深度学习和热交换法的导电型氧化镓预测方法，其特征在于，所述根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，得到预处理的制备数据，包括：
    根据所述籽晶数据、所述环境数据、所述控制数据以及所述原料数据，确定制备向量；其中，所述制备向量中第一元素为所述籽晶衍射峰半高宽、所述籽晶衍射峰半高宽偏差值以及所述籽晶直径中的一个，所述制备向量中第二元素为所述保温层热阻值、保温层热阻值偏差值以及保温层形状因子中的一个，所述制备向量中第三元素为所述线圈输入功率、所述线圈冷却功率以及所述籽晶冷却介质 流量中的一个；所述制备向量中第四元素为所述掺杂类型数据和所述导电掺杂浓度中的一个；
    根据所述制备向量，确定所述预处理的制备数据。
  5. 根据权利要求1所述的基于深度学习和热交换法的导电型氧化镓预测方法，其特征在于，所述预测性质数据还包括：预测裂纹数据、预测杂晶数据、预测衍射峰半高宽、预测衍射峰半高宽径向偏差值、预测衍射峰半高宽轴向偏差值、预测载流子浓度径向偏差值以及预测载流子浓度轴向偏差值。
  6. 一种基于深度学习和热交换法的导电型氧化镓制备方法，其特征在于，所述制备方法包括：
    获取目标导电型氧化镓单晶的目标性质数据；所述目标性质数据包括：目标载流子浓度；
    根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据；其中，所述目标制备数据包括：籽晶数据、环境数据、控制数据以及原料数据；所述控制数据包括：籽晶冷却介质流量，所述原料数据包括：掺杂类型数据和导电掺杂浓度；
    基于热交换法，根据所述目标制备数据制备得到目标导电型氧化镓单晶。
  7. 根据权利要求6所述的基于深度学习和热交换法的导电型氧化镓制备方法，其特征在于，所述根据所述目标性质数据以及训练好的神经网络模型，确定所述目标导电型氧化镓单晶对应的目标制备数据，包括：
    获取预设制备数据，对所述预设制备数据进行预处理，得到预处理的预设制备数据；
    将所述预处理的预设制备数据输入训练好的神经网络模型，通过所述训练好的神经网络模型得到所述导电型氧化镓单晶对应的预测性质数据；
    根据所述预测性质数据、所述目标性质数据，对所述预设制备数据进行修正，以得到所述目标导电型氧化镓单晶对应的目标制备数据。
  8. 根据权利要求6所述的基于深度学习和热交换法的导电型氧化镓制备方法，其特征在于，所述训练好的神经网络模型采用如下步骤训练得到：
    获取导电型氧化镓单晶的训练数据以及所述训练数据对应的实际性质数据；其中，所述训练数据包括：籽晶训练数据、环境训练数据、控制训练数据以及原 料训练数据；所述控制训练数据包括：籽晶冷却介质流量训练数据，所述原料训练数据包括：掺杂类型数据和导电掺杂浓度；
    对所述训练数据进行预处理，得到预处理的训练数据；
    将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据；所述预测生成性质数据包括：预测生成载流子浓度；
    根据所述预测生成性质数据以及所述实际性质数据调整所述预设的神经网络模型的模型参数进行修正，以得到训练好的神经网络模型。
  9. 根据权利要求8所述的基于深度学习和热交换法的导电型氧化镓制备方法，其特征在于，所述预设的神经网络模型包括：特征提取模块和全连接模块，
    所述将所述预处理的训练数据输入预设的神经网络模型，通过所述预设的神经网络模型得到所述预处理的训练数据对应的预测生成性质数据，包括：
    将所述预处理的训练数据输入所述特征提取模块，通过所述特征提取模块得到所述预处理的训练数据对应的特征向量；
    将所述特征向量输入所述全连接模块，通过所述全连接模块得到所述预处理的训练数据得到的预测生成性质数据。
  10. 一种基于深度学习和热交换法的导电型氧化镓制备系统，其特征在于，包括存储器和处理器，所述存储器存储有计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至5中任一项所述的预测方法的步骤，或权利要求6至9中任一项所述的制备方法的步骤。
PCT/CN2021/076073 2020-12-31 2021-02-08 一种基于深度学习和热交换法的导电型氧化镓制备方法 WO2022141766A1 (zh)

Priority Applications (3)

Application Number Priority Date Filing Date Title
US18/250,262 US20230399768A1 (en) 2020-12-31 2021-02-08 Preparation method of conductive gallium oxide based on deep learning and heat exchange method
JP2023526453A JP2023547495A (ja) 2020-12-31 2021-02-08 ディープラーニングと熱交換法による導電性酸化ガリウムの製造方法
EP21912538.2A EP4206990A4 (en) 2020-12-31 2021-02-08 METHOD FOR PRODUCING CONDUCTIVE GALLIUM OXIDE BASED ON DEEP LEARNING AND HEAT EXCHANGE METHODS

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202011641376.2A CN112853468A (zh) 2020-12-31 2020-12-31 一种基于深度学习和热交换法的导电型氧化镓制备方法
CN202011641376.2 2020-12-31

Publications (1)

Publication Number Publication Date
WO2022141766A1 true WO2022141766A1 (zh) 2022-07-07

Family

ID=76000791

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/076073 WO2022141766A1 (zh) 2020-12-31 2021-02-08 一种基于深度学习和热交换法的导电型氧化镓制备方法

Country Status (5)

Country Link
US (1) US20230399768A1 (zh)
EP (1) EP4206990A4 (zh)
JP (1) JP2023547495A (zh)
CN (1) CN112853468A (zh)
WO (1) WO2022141766A1 (zh)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114561701B (zh) * 2021-06-07 2022-08-19 浙江大学杭州国际科创中心 一种铸造法生长氧化镓单晶的方法及包含氧化镓单晶的半导体器件

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006312571A (ja) * 2005-05-09 2006-11-16 Koha Co Ltd Ｇａ２Ｏ３系結晶の製造方法
CN111028893A (zh) * 2019-10-28 2020-04-17 山东天岳先进材料科技有限公司 一种晶体生长预测方法及装置
CN111368434A (zh) * 2020-03-05 2020-07-03 包头美科硅能源有限公司 一种基于ann的提拉法单晶硅固液界面的预测方法
CN111695287A (zh) * 2020-04-22 2020-09-22 山东天岳先进材料科技有限公司 一种预测SiC单晶炉内整体温度场的方法及设备

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6376600B2 (ja) * 2015-03-20 2018-08-22 株式会社タムラ製作所 結晶積層構造体の製造方法
CN105603528B (zh) * 2016-03-04 2018-07-27 同济大学 一种具有热释光性能的氧化镓晶体及其制备方法
CN107541776A (zh) * 2017-08-14 2018-01-05 同济大学 一种大尺寸氧化镓单晶的生长设备及方法
CN107885853A (zh) * 2017-11-14 2018-04-06 同济大学 一种基于深度学习的组合式文本分类方法
CN109537055A (zh) * 2019-01-28 2019-03-29 山东大学 一种半绝缘氧化镓晶体及其制备方法

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006312571A (ja) * 2005-05-09 2006-11-16 Koha Co Ltd Ｇａ２Ｏ３系結晶の製造方法
CN111028893A (zh) * 2019-10-28 2020-04-17 山东天岳先进材料科技有限公司 一种晶体生长预测方法及装置
CN111368434A (zh) * 2020-03-05 2020-07-03 包头美科硅能源有限公司 一种基于ann的提拉法单晶硅固液界面的预测方法
CN111695287A (zh) * 2020-04-22 2020-09-22 山东天岳先进材料科技有限公司 一种预测SiC单晶炉内整体温度场的方法及设备

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of EP4206990A4 *

Also Published As

Publication number Publication date
JP2023547495A (ja) 2023-11-10
CN112853468A (zh) 2021-05-28
EP4206990A1 (en) 2023-07-05
EP4206990A4 (en) 2024-04-03
US20230399768A1 (en) 2023-12-14

Similar Documents

Publication Publication Date Title
WO2022141766A1 (zh) 一种基于深度学习和热交换法的导电型氧化镓制备方法
WO2022141752A1 (zh) 一种基于深度学习和提拉法的导电型氧化镓的质量预测方法、制备方法及系统
WO2022141758A1 (zh) 一种基于深度学习和提拉法的高阻型氧化镓的质量预测方法、制备方法及系统
JP7478819B2 (ja) ディープラーニング及びｅｆｇテクニック方法による高抵抗型酸化ガリウムの品質予測方法、製造方法及びそのシステム
WO2022141756A1 (zh) 一种基于深度学习和坩埚下降法的氧化镓制备方法及系统
WO2022141751A1 (zh) 一种基于深度学习和坩埚下降法的高阻型氧化镓制备方法
WO2022141768A1 (zh) 一种基于深度学习和导模法的氧化镓的质量预测方法、制备方法及系统
WO2022141755A1 (zh) 一种基于深度学习和热交换法的高阻型氧化镓制备方法
WO2022141750A1 (zh) 一种基于深度学习和热交换法的氧化镓制备方法及系统
WO2022141760A1 (zh) 一种基于深度学习和坩埚下降法的导电型氧化镓制备方法
WO2022141764A1 (zh) 一种基于深度学习和导模法的导电型氧化镓的质量预测方法、制备方法及系统
WO2022141759A1 (zh) 一种基于深度学习和提拉法的氧化镓的质量预测方法、制备方法及系统
JPWO2022141758A5 (zh)
JPWO2022141752A5 (zh)

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21912538

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2021912538

Country of ref document: EP

Effective date: 20230328

WWE Wipo information: entry into national phase

Ref document number: 2023526453

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE