Machine learning-based noise filtering for rapid scan STEM image and its application to in-situ 3D dislocation observation

KAKEN

About This Project

Japan Grant Number
JP21K20491 (JGN)
Funding Program
Grants-in-Aid for Scientific Research
Funding Organization
Japan Society for the Promotion of Science

Kakenhi Information

Project/Area Number
21K20491
Research Category
Grant-in-Aid for Research Activity Start-up
Allocation Type
  • Multi-year Fund
Review Section / Research Field
  • 0401:Materials engineering, chemical engineering, and related fields
Research Institution
  • Kyushu University
Project Period (FY)
2021-08-30 〜 2023-03-31
Project Status
Completed
Budget Amount*help
3,120,000 Yen (Direct Cost: 2,400,000 Yen Indirect Cost: 720,000 Yen)

Research Abstract

金属材料の変形や破壊を詳細に理解するためには格子欠陥の挙動を捉える必要があり，本研究ではこのための技術開発およびその実施を行う．高速で格子欠陥の挙動を捉えるためには，高速で電子顕微鏡観察を行う必要があるが，高速で撮影を行うと像にノイズが多く含まれてしまう．そこで，機械学習を用いて像の改善を行い，これまで未解明な点が多かった変形中に変化する格子欠陥の詳細を可視化することを試みる．

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