配备人工智能的扫描探针显微镜可在室温下自主进行特定位点的原子级表征。

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL Small Methods Pub Date : 2024-09-06 DOI:10.1002/smtd.202400813
Zhuo Diao, Keiichi Ueda, Linfeng Hou, Fengxuan Li, Hayato Yamashita, Masayuki Abe
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引用次数: 0

摘要

本文介绍了一种先进的人工智能扫描探针显微镜系统(AI-SPM),该系统专为自动驾驶原子尺度测量而设计。该系统能以高精度专业地识别和操纵原子位置,自主执行光谱数据采集和原子调整等任务。AI-SPM 的一个突出特点是能够检测和适应表面缺陷,在必要时瞄准或避开它们。此外,它还能克服热效应导致的位置漂移和尖端顶点原子变化等典型挑战,确保针对特定部位进行准确的表面分析。在室温的苛刻条件下进行的测试证明了该系统的稳健性,成功地克服了热漂移和针尖波动。在对 Si(111)-(7 × 7) 表面进行这些测试期间,AI-SPM 自动识别了无缺陷区域,并在不同的原子位点进行了大量的电流电压光谱测量,同时自动补偿热漂移并监控探针的健康状况。这些实验产生了对可靠的材料表征至关重要的大量数据集,并证明了 AI-SPM 在显著改善数据采集方面的潜力。将人工智能集成到 SPM 技术中代表着向更有效、精确和可靠的原子级表面分析迈出了一步,从而彻底改变了材料表征方法。
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AI-Equipped Scanning Probe Microscopy for Autonomous Site-Specific Atomic-Level Characterization at Room Temperature.

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.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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