AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature

Diao, Zhuo; Ueda, Keiichi; Hou, Linfeng et al.

Small Methods, 2024, 9(1), 2400813

アクセス数:519件（2025-07-12 20:40 集計）

固定URL: https://hdl.handle.net/11094/98303

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論文情報

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タイトル
AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature
著者
researchmap permalink
diaozhuo diaozhuo
著者名
Diao, Zhuo
著者名
Ueda, Keiichi
著者名
Hou, Linfeng
著者名
Li, Fengxuan
NRID
1000010595440 1000010595440
ORCID
0000-0003-4473-5773 0000-0003-4473-5773
researchmap permalink
10595440 10595440
Scopus Author ID
57202699112 57202699112
著者名
Yamashita, Hayato
NRID
1000000362666 1000000362666
ORCID
0000-0001-5619-3911 0000-0001-5619-3911
researchmap permalink
read0089681 read0089681
Scopus Author ID
7404123577 7404123577
著者名
Abe, Masayuki
キーワード等
deep learning
room temperature
scanning probe microscopy
scanning tunneling spectroscopy
self-driving
内容
Diao Z., Ueda K., Hou L., et al. AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature. Small Methods, (2024); https://doi.org/10.1002/smtd.202400813.
抄録
An advanced scanning probe microscopy system enhanced with artificial intelligence (AI-SPM) designed for self-driving atomic-scale measurements is presented. This system expertly identifies and manipulates atomic positions with high precision, autonomously performing tasks such as spectroscopic data acquisition and atomic adjustment. An outstanding feature of AI-SPM is its ability to detect and adapt to surface defects, targeting or avoiding them as necessary. It is also designed to overcome typical challenges such as positional drift and tip apex atomic variations due to the thermal effects, ensuring accurate, site-specific surface analysis. The tests under the demanding conditions of room temperature have demonstrated the robustness of the system, successfully navigating thermal drift and tip fluctuations. During these tests on the Si(111)-(7 × 7) surface, AI-SPM autonomously identified defect-free regions and performed a large number of current–voltage spectroscopy measurements at different adatom sites, while autonomously compensating for thermal drift and monitoring probe health. These experiments produce extensive data sets that are critical for reliable materials characterization and demonstrate the potential of AI-SPM to significantly improve data acquisition. The integration of AI into SPM technologies represents a step toward more effective, precise and reliable atomic-level surface analysis, revolutionizing materials characterization methods.
出版者
John Wiley and Sons Inc
収録物名
Small Methods
巻(号)
9(1)
ページ
2400813
刊行年月
2024-09-06
言語
英語
ハンドルURL
https://hdl.handle.net/11094/98303
eISSN
23669608
関連情報 (isIdenticalTo)
DOI (出版社版)
https://doi.org/10.1002/smtd.202400813
アクセス権
open access
権利情報
This article is licensed under a Creative Commons Attribution 4.0 International License.
出版タイプ
Version of Record
カテゴリ
学術雑誌論文 Journal Article