Robust procedure for creating and characterizing the atomic structure of scanning tunneling microscope tips

Tewari, Sumit; Bastiaans, Koen M.; Allan, Milan P.; Ruitenbeek, J. M. van

doi:10.3762/bjnano.8.238

Cited by 13 publications

(24 citation statements)

References 31 publications

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“…As before, performance was verified by iteratively predicting additional lines of the i images in the holdout set and calculating the cumulative predictions using equation (2). This is shown in figure 6.…”

Section: Individual Linescan Windows and Cumulative Averagesmentioning

confidence: 96%

“…This window could then be iteratively rolled while an image is being generated. We therefore modify equation (2), and use cumulative averaging to make predictions after each successive linescan from j=W+1 to j=N…”

Section: Multiple Linescan Windows and Lstmmentioning

confidence: 99%

“…One of the major challenges in the drive to fully automate the scanning probe microscope is the need to constantly maintain the integrity of the tip [1,2]. During an experimental session, interactions with the surface can cause the tip to spontaneously and randomly change shape, modifying the interactions and therefore changing the data acquired in a highly nonlinear fashion.…”

Section: Introductionmentioning

confidence: 99%

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Embedding human heuristics in machine-learning-enabled probe microscopy

Gordon

Junqueira

Moriarty

2020

Mach. Learn.: Sci. Technol.

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Scanning probe microscopists generally do not rely on complete images to assess the quality of data acquired during a scan. Instead, assessments of the state of the tip apex, which not only determines the resolution in any scanning probe technique, but can also generate a wide array of frustrating artefacts, are carried out in real time on the basis of a few lines of an image (and, typically, their associated line profiles.) The very small number of machine learning approaches to probe microscopy published to date, however, involve classifications based on full images. Given that data acquisition is the most time-consuming task during routine tip conditioning, automated methods are thus currently extremely slow in comparison to the tried-and-trusted strategies and heuristics used routinely by probe microscopists. Here, we explore various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans. By employing a secondary temporal network and a rolling window of a small group of individual scanlines, we find that tip assessment is possible with a small fraction of a complete image. We achieve this with little-to-no performance penalty-or, indeed, markedly improved performance in some cases-and introduce a protocol to detect the state of the tip apex in real time.

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Section: Individual Linescan Windows and Cumulative Averagesmentioning

confidence: 96%

Section: Multiple Linescan Windows and Lstmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Embedding human heuristics in machine-learning-enabled probe microscopy

Gordon

Junqueira

Moriarty

2020

Mach. Learn.: Sci. Technol.

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“…Machine learning offers the opportunity for a distinctive and disruptive departure from the type of experimental (and, indeed, theoretical) methodologies that have formed the bedrock of the field of scanning probe microscopy ever since its inception in the eighties [15,16]. Almost forty years since the invention of the technique, it is still the case that the patience of the experimentalist remains the key parameter in cajoling the apex of the tip into yielding (sub)molecular or (sub)atomic [17][18][19] resolution. Even then, unless an 'inverse imaging' [20][21][22] strategy is adopted, the atomistic structure of the apex remains unknown.…”

Section: Taking the Pain Out Of Probesmentioning

confidence: 99%

Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

Gordon

Moriarty

2020

Mach. Learn.: Sci. Technol.

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We discuss the exciting prospects for a step change in our ability to map and modify matter at the atomic/molecular level by embedding machine learning algorithms in scanning probe microscopy (with a particular focus on scanning tunnelling microscopy, STM). This nano-AI hybrid approach has the far-reaching potential to realise a technology capable of the automated analysis, actuation, and assembly of matter with a precision down to the single chemical bond limit.

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“…We use standard tip cleaning procedures, such as voltage pulsing up to −3 V and mechanical annealing [6]. The latter procedure consists of repeated and controlled indentation of the tip into the surface up to several conductance quanta and leads to a crystalline, atomically sharp tip apex [7]. Figure 2(e) shows a typical STM image (99 × 99 nm) of the reconstructed Au(111) surface obtained with the fabricated tip (setup conditions 100 mV, 300 pA).…”

mentioning

confidence: 99%

Nanofabricated tips for device-based scanning tunneling microscopy

et al. 2019

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We report on the fabrication and performance of a new kind of tip for scanning tunneling microscopy. By fully incorporating a metallic tip on a silicon chip using modern micromachining and nanofabrication techniques, we realize so-called smart tips and show the possibility of device-based STM tips. Contrary to conventional etched metal wire tips, these can be integrated into lithographically defined electrical or photonic circuits, as well as mechanical systems. We experimentally demonstrate that the performance of the smart tips is on par with conventional ones, both in stability and resolution. In situ tip preparation methods are possible and we verify that they can resolve the herringbone reconstruction and Friedel oscillations on Au (111) surfaces. In addition, these devices can be made to accommodate two isolated tips with sub-50 nm apex-to-apex distance to measure electron correlations at the nanoscale using a new type of double-tip experiment described in this letter.Scanning tunneling microscopy (STM) is one of the leading tools for probing electronic and topographic information at the atomic scale [1]. Since its inception a few decades ago, data quality has dramatically improved by focusing on mechanical stability, tip preparation and lower temperatures. New possibilities have emerged and greatly extended the range of STM, including quasi-particle interference studies with density of states mapping [2, 3], spin-polarized STM [4], and ultralow temperature operation [5].Here, we introduce a platform for bringing new, devicebased functionality to STM, in order to utilize decades of progress in device engineering for the field of scanning probe. We replace the conventional electrochemically etched, pointy metal wire with an integrated metal tip on a silicon chip. This new platform, which we call smart tip, allows in principle to directly add additional capabilities to a STM tip, including novel spin-sensitivity, local heating, local magnetic fields, local gating, highfrequency compatible coplanar waveguides, resonators, qubits, and double tips. However, it is a priori unclear, whether such a nanofabricated tip will function for STM measurements, as several challenges arise: the stability needs to be below the picometer scale, stringent requirements exist on the shape and sharpness of the freestanding tip, and contamination from fabrication residues need to be absent. In this letter, we demonstrate the feasibility of the novel smart tip platform. We first discuss our newly developed fabrication procedure and then experimentally show the functionality of these tips in standard STM measurements. Finally, we present a theory of how such nanofabricated double tips will allow to measure electron correlations, if the tip-to-tip distance is on the * s.groeblacher@tudelft.nl † allan@physics.leidenuniv.nl

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Robust procedure for creating and characterizing the atomic structure of scanning tunneling microscope tips

Cited by 13 publications

References 31 publications

Embedding human heuristics in machine-learning-enabled probe microscopy

Embedding human heuristics in machine-learning-enabled probe microscopy

Machine learning at the (sub)atomic scale: next generation scanning probe microscopy

Nanofabricated tips for device-based scanning tunneling microscopy

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