A method for preparing conductive gallium oxide based on deep learning and crucible lowering method
By combining deep learning and neural network models with the crucible lowering method, the problem of being unable to prepare conductive gallium oxide with a predetermined carrier concentration in existing technologies has been solved, realizing precise control of the carrier concentration and performance prediction of gallium oxide single crystals.
CN112863619BActive Publication Date: 2026-07-17HANGZHOU FUJIA GALLIUM TECH CO LTD
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HANGZHOU FUJIA GALLIUM TECH CO LTD
- Filing Date
- 2020-12-31
- Publication Date
- 2026-07-17
AI Technical Summary
Technical Problem
Existing technologies cannot prepare conductive gallium oxide with a predetermined carrier concentration using the crucible descent method.
Method used
By combining deep learning and the crucible descent method with a neural network model, the preparation data is acquired and preprocessed. The trained neural network model is then used to predict the carrier concentration of conductive gallium oxide, and the preparation parameters are adjusted to obtain the target conductive gallium oxide single crystal.
Benefits of technology
It enables precise prediction and control of carrier concentration in conductive gallium oxide single crystals, ensuring that the fabrication process can meet the predetermined performance requirements.
✦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN112863619B_ABST
Abstract
The application relates to a conductive gallium oxide preparation method based on deep learning and a crucible lowering method. A prediction method comprises the following steps: acquiring preparation data of a conductive gallium oxide single crystal; the preparation data comprises seed crystal data, environment data, control data and raw material data; the raw material data comprises doping type data and conductive doping concentration; the preparation data is pretreated; the pretreated preparation data is input into a trained neural network model to obtain corresponding prediction property data of the conductive gallium oxide single crystal; the prediction property data comprises a predicted carrier concentration. The preparation data is pretreated first to obtain pretreated preparation data, the pretreated preparation data is input into the trained neural network model to obtain corresponding prediction property data of the conductive gallium oxide single crystal, and the conductive gallium oxide with a predetermined carrier concentration can be obtained by adjusting the preparation data.
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