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

Diao, Zhuo; Ueda, Keiichi; Hou, Linfeng; Li, Fengxuan; Yamashita, Hayato; Abe, Masayuki

doi:https://doi.org/10.1002/smtd.202400813

アクセス数:19件（2025-01-03 08:33 集計）

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

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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
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

資源タイプ	学術雑誌論文
ローカル資源タイプ	学術雑誌論文
DCTERMS.bibliographicCitation	Small Methods. 2024, p. 2400813
DC.title	AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature
DC.creator	Diao, Zhuo
	Ueda, Keiichi
	Hou, Linfeng
	Li, Fengxuan
	Yamashita, Hayato
	Abe, Masayuki
DC.publisher	John Wiley and Sons Inc
DC.language' scheme='DCTERMS.RFC1766	英語
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-09-06
DC.identifier	https://doi.org/10.1002/smtd.202400813
DC.subject	deep learning
	room temperature
	scanning probe microscopy
	scanning tunneling spectroscopy
	self-driving
DC.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.
DCTERMS.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.
DC.rights	This article is licensed under a Creative Commons Attribution 4.0 International License.
DC.format	98303
citation_title	AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature
citation_author	Diao, Zhuo
	Ueda, Keiichi
	Hou, Linfeng
	Li, Fengxuan
	Yamashita, Hayato
	Abe, Masayuki
citation_publisher	John Wiley and Sons Inc
citation_language	英語
citation_date	2024-09-06
citation_journal_title	Small Methods
citation_firstpage	2400813
citation_doi	https://doi.org/10.1002/smtd.202400813
citation_public_url	https://hdl.handle.net/11094/98303

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