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

Number of Access:571（2025-10-24 23:16 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/98303

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Title
AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature
Creator
researchmap permalink
diaozhuo diaozhuo
CreatorName
Diao, Zhuo
CreatorName
Ueda, Keiichi
CreatorName
Hou, Linfeng
CreatorName
Li, Fengxuan
NRID
1000010595440 1000010595440
ORCID
0000-0003-4473-5773 0000-0003-4473-5773
researchmap permalink
10595440 10595440
Scopus Author ID
57202699112 57202699112
CreatorName
Yamashita, Hayato
NRID
1000000362666 1000000362666
ORCID
0000-0001-5619-3911 0000-0001-5619-3911
researchmap permalink
read0089681 read0089681
Scopus Author ID
7404123577 7404123577
CreatorName
Abe, Masayuki
Subject
deep learning
room temperature
scanning probe microscopy
scanning tunneling spectroscopy
self-driving
Description
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.
Abstract
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.
Publisher
John Wiley and Sons Inc
Source Title
Small Methods
Volume (Issue)
9(1)
Page
2400813
Date of Issued
2024-09-06
Language
English
Handle URL
https://hdl.handle.net/11094/98303
eISSN
23669608
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.1002/smtd.202400813
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article