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arXiv 2408.04055cond-mat.mes-hallcond-mat.mtrl-scics.AIcs.LG
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基于机器学习的奖励驱动扫描探针显微镜调谐:迈向全自动显微镜

Machine Learning-Based Reward-Driven Tuning of Scanning Probe Microscopy: Towards Fully Automated Microscopy

University of Tennessee(田纳西大学);Oxford Instruments Asylum Research(牛津仪器庇护研究公司);Pacific Northwest National Laboratory(太平洋西北国家实验室);National Cheng Kung University(国立成功大学);University of Duisburg-Essen(杜伊斯堡-埃森大学)

Yu Liu, Roger Proksch, Jason Bemis, Utkarsh Pratiush, Astita Dubey, Mahshid Ahmadi, Reece Emery, Philip D. Rack, Yu-Chen Liu, Jan-Chi Yang, Sergei V. Kalinin

AI总结:

本文提出一种奖励驱动的机器学习工作流程,通过编码物理与经验知识的奖励函数自动优化轻敲模式扫描探针显微镜参数,实现高质量成像并推动全自动SPM发展。

摘要:

自扫描探针显微镜(SPM)问世以来,轻敲或间歇接触模式一直是最广泛使用的成像模式之一。轻敲模式的手动优化不仅耗费大量仪器和操作员时间,而且对于新型样品或经验不足的用户,还经常导致探针和样品频繁损坏、图像质量差以及可重复性问题。尽管应用广泛,轻敲模式成像的优化是一个极其困难的问题,既不适合经典控制方法,也不适合机器学习。在此,我们引入一种奖励驱动的工作流程,以实现轻敲模式下SPM优化的自动化。奖励函数基于多个通道定义,并编码了良好扫描的物理和经验知识,代表一种与样品无关的图像质量度量,并模仿人类操作员所采用的决策逻辑。该自动化工作流程可为不同的探针和样品提供最优扫描参数,并在吸引力模式下持续获得高质量的SPM图像。这项研究拓宽了SPM的应用范围和可及性,并为全自动SPM打开了大门。

英文摘要:

Since the dawn of scanning probe microscopy (SPM), tapping or intermittent contact mode has been one of the most widely used imaging modes. Manual optimization of tapping mode not only takes a lot of instrument and operator time, but also often leads to frequent probe and sample damage, poor image quality and reproducibility issues for new types of samples or inexperienced users. Despite wide use, optimization of tapping mode imaging is an extremely hard problem, ill-suited to either classical control methods or machine learning. Here we introduce a reward-driven workflow to automate the optimization of SPM in the tapping mode. The reward function is defined based on multiple channels with physical and empirical knowledge of good scans encoded, representing a sample-agnostic measure of image quality and imitating the decision-making logic employed by human operators. This automated workflow gives optimal scanning parameters for different probes and samples and gives high-quality SPM images consistently in the attractive mode. This study broadens the application and accessibility of SPM and opens the door for fully automated SPM.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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