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M., Junqueira, F. L. Q., +& Moriarty, P. J. (2020). Embedding human heuristics in +machine-learning-enabled probe microscopy. Machine Learning: Science and +Technology, 1(1), 015001. +https://doi.org/10.1088/2632-2153/ab42ec + + + Automated tip conditioning for scanning +tunneling spectroscopy + Wang + The Journal of Physical Chemistry +A + 6 + 125 + 10.1021/acs.jpca.0c10731 + 2021 + Wang, S., Zhu, J., Blackwell, R., +& Fischer, F. R. (2021). Automated tip conditioning for scanning +tunneling spectroscopy. The Journal of Physical Chemistry A, 125(6), +1384–1390. +https://doi.org/10.1021/acs.jpca.0c10731 + + + Artificial-intelligence-driven scanning probe +microscopy + Krull + Communications Physics + 1 + 3 + 10.1038/s42005-020-0317-3 + 2020 + Krull, A., Hirsch, P., Rother, C., +& Schiffrin, A. (2020). Artificial-intelligence-driven scanning +probe microscopy. Communications Physics, 3(1). +https://doi.org/10.1038/s42005-020-0317-3 + + + New-horizons-SPM/nanonisTCP: nanonisTCP +v1.0.0 + Ceddia + 10.5281/zenodo.7402665 + 2022 + Ceddia, J., jhellerstedt, & +benlowe1. (2022). New-horizons-SPM/nanonisTCP: nanonisTCP v1.0.0 +(Version v1.0.0). Zenodo. +https://doi.org/10.5281/zenodo.7402665 + + + Autonomous scanning probe microscopy in situ +tip conditioning through machine learning + Rashidi + ACS Nano + 6 + 12 + 10.1021/acsnano.8b02208 + 2018 + Rashidi, M., & Wolkow, R. A. +(2018). Autonomous scanning probe microscopy in situ tip conditioning +through machine learning. ACS Nano, 12(6), 5185–5189. +https://doi.org/10.1021/acsnano.8b02208 + + + Mimea nanonis + Specs-GmBH + 2015 + Specs-GmBH. (2015). 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b/joss.06028/paper.jats/10.21105.joss.06028.jats @@ -0,0 +1,345 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6028 +10.21105/joss.06028 + +Scanbot: An STM Automation Bot + + + +https://orcid.org/0000-0003-3990-8852 + +Ceddia +Julian + + + +* + + +https://orcid.org/0000-0003-2282-8223 + +Hellerstedt +Jack + + + + + +https://orcid.org/0000-0002-5157-7737 + +Lowe +Benjamin + + + + + +https://orcid.org/0000-0003-1140-8485 + +Schiffrin +Agustin + + + + + + +School of Physics & Astronomy, Monash University, +Clayton, Victoria 3800, Australia + + + + +ARC Centre of Excellence in Future Low-Energy Electronics +Technologies, Monash University, Clayton, Victoria 3800, +Australia + + + + +* E-mail: + + +13 +3 +2023 + +9 +99 +6028 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Scanning Tunneling Microscopy +STM +Automation + + + + + + Summary +

Scanning Tunnelling Microscopes (STM) are capable of capturing + images of surfaces with atomic-scale resolution. This is achieved by + scanning an atomically sharp probe across the surface of the sample + while monitoring an electric current. However, the quality of STM data + relies heavily on the atomic-scale geometry and composition of the + scanning probe apex, as well as the roughness and cleanliness of the + scanned region. For instance, blunt tips result in blurry images while + contaminated tips can lead to noisy images due to interactions with + the sample. As a result, optimal STM data acquisition commonly + requires time-consuming tasks such as probe conditioning—i.e., + sharpening via “tip-shaping”, where the apex of the probe can be + refined by poking it into a clean metal surface—and identification of + areas of interest of the sample. Moreover, the quality of the probe + can vary during a scan, especially when scanning over debris or + excessively rough areas, necessitating additional tip-shaping.

+

Here, we present Scanbot, a program that fully automates common STM + data acquisition techniques, as well as tip-shaping and sample + surveying. Scanbot relies on a dual sample holder (DSH; + [fig:1]), where a sample of + interest is mounted alongside a clean reference metal surface, which + is ideal for tip preparation. Scanbot is able to analyse STM images + and identify when the probe requires conditioning, subsequently moving + it from the sample of interest to the clean reference metal, where it + will prepare a scanning probe capable of obtaining high-quality STM + images. This is accomplished using built-in piezoceramic scanners to + maneuver the STM tip while tracking its position through a camera + feed; [fig:1]b). Once + Scanbot determines that the probe has been conditioned adequately, it + moves the tip back to the sample of interest and STM data acquisition + resumes.

+ +

Tracking and maneuvering the STM probe above the dual + sample holder (DSH). a) Schematic of the STM tip over + the dual sample holder setup. A sample of interest is mounted next + to a clean reference metal substrate (e.g. Au(111)) which is ideal + for tip shaping. b) Image from the camera feed used by + Scanbot to track and maneuver the STM probe automatically from the + sample to the clean reference metal, where it can be refined. The + red (green) marker indicates the probe apex position (target + position, respectively). See Scanbot + documentation + for a video example. +

+ +
+

[fig:2] demonstrates + Scanbot’s ability to recondition a ‘bad’ tip on a clean reference + metal surface. Scanbot can gently impinge the scanning probe apex onto + a clean, flat region of the metal surface, which results in an imprint + associated with the geometry of the tip. This imprint can then be + scanned, and the resulting image is similar to the auto-correlation + function of the tip’s apex. The quality of the tip can be assessed by + measuring the area and circularity of the imprint. If the imprint does + not meet the desired criteria, a more aggressive tip shaping action is + carried out, and the process is repeated until a high-quality tip is + achieved.

+ +

Successive STM images (left to right) of the tip’s + imprint on a clean metal surface, each following a more agressive + tip-shaping action in a different location. The area and circularity + of each imprint reflects the geometry of the apex of the scanning + probe. Thus the process is repeated until a desired geometry is + achieved.

+ +
+
+ + Statement of need +

To reduce the time-intensive nature of STM experiments, various + innovative solutions have been implemented to automate specific tasks. + For instance, Wang et al. created a Python package that automates + probe conditioning for Scanning Tunneling Spectroscopy + (Wang + et al., 2021). However, this package still requires manual + preparation of the tip such that it can acquire clean images. Some + researchers have employed the use of machine learning algorithms to + analyse acquired images and determine when a probe needs refining + (Gordon + et al., 2020; + Rashidi + & Wolkow, 2018), then Reinforcement Learning (RL) agents + can condition the probe accordingly + (Krull + et al., 2020). Although these approaches have significantly + advanced automation in STM experiments, they are often tailored to + specific surfaces and STM equipment, making it challenging to transfer + them directly to other labs studying different kinds of samples or + working with different STM systems.

+

To overcome these limitations, we have developed Scanbot, a Python + robot that is compatible with a broader range of STMs, specifically + those compatible with the Nanonis V5 software + (Ceddia + et al., 2022; + Specs-GmBH, + 2015). Additionally, our package incorporates Scanbot’s + distinctive approach to tip shaping, which involves monitoring the + tip’s motion above a dual sample holder. This method is particularly + beneficial in experiments where the sample’s properties might make it + challenging to achieve a high-quality scanning probe without needing + to manually switch out the sample for a clean metal on which the tip + can be prepared.

+

Scanbot has been developed in a modular fashion, which means its + functionality can easily be expanded or improved through contributions + from the open-source community. Furthermore, through the use of + hooks, + users can customise or replace key funcionalities that are system- or + lab-specific, without rewriting Scanbot’s source code. This has the + advantage of being able to update Scanbot to the latest version + without losing customised code. Such hooks can also be used to improve + Scanbot’s existing functionality or test potential new features. For + instance, Scanbot’s algorithmic approach to automated tip shaping + might benefit the integration of an RL agent. This could be achieved + by leveraging the hook + hk_tipShape, + where important parameters related to tip shaping can be adjusted + based on images of the tip’s imprint. Complete documentation for + Scanbot, including how such hooks can be leveraged, can be found at + https://new-horizons-spm.github.io/scanbot.

+
+ + Acknowledgements +

A.S. acknowledges funding support from the ARC Future Fellowship + scheme (FT150100426). J.C., B.L., and J.H. acknowledge funding support + from the Australian Research Council (ARC) Centre of Excellence in + Future Low-Energy Electronics Technologies (CE170100039). J.C., and + B.L. are supported through an Australian Government Research Training + Program (RTP) Scholarship.

+
+ + + + + + + + GordonOliver M + JunqueiraFilipe L Q + MoriartyPhilip J + + Embedding human heuristics in machine-learning-enabled probe microscopy + Machine Learning: Science and Technology + IOP Publishing + 202002 + 1 + 1 + https://dx.doi.org/10.1088/2632-2153/ab42ec + 10.1088/2632-2153/ab42ec + 015001 + + + + + + + WangShenkai + ZhuJunmian + BlackwellRaymond + FischerFelix R. + + Automated tip conditioning for scanning tunneling spectroscopy + The Journal of Physical Chemistry A + 2021 + 125 + 6 + + https://doi.org/10.1021/acs.jpca.0c10731 + + + 10.1021/acs.jpca.0c10731 + 1384 + 1390 + + + + + + KrullA. + HirschP. + RotherC. + SchiffrinA. + + Artificial-intelligence-driven scanning probe microscopy + Communications Physics + 2020 + 3 + 1 + https://doi.org/10.1038/s42005-020-0317-3 + 10.1038/s42005-020-0317-3 + + + + + + CeddiaJulian + jhellerstedt + benlowe1 + + New-horizons-SPM/nanonisTCP: nanonisTCP v1.0.0 + Zenodo + 202212 + https://doi.org/10.5281/zenodo.7402665 + 10.5281/zenodo.7402665 + + + + + + RashidiMohammad + WolkowRobert A. + + Autonomous scanning probe microscopy in situ tip conditioning through machine learning + ACS Nano + 2018 + 12 + 6 + + https://doi.org/10.1021/acsnano.8b02208 + + + 10.1021/acsnano.8b02208 + 5185 + 5189 + + + + + + Specs-GmBH + + Mimea nanonis + 2015 + https://www.specs-group.com/nanonis/products/mimea/ + + + + +

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