Measuring the reactivity of a silicon-terminated probe

Sweetman, Adam; Stirling, Julian; Jarvis, Samuel; Rahe, Philipp; Moriarty, Philip

doi:10.1103/physrevb.94.115440

Cited by 8 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Whilst these particular features can be seen when scanning any surface, others are specific to the surface being investigated [21]. For example, for the H:Si(100) surface, four different, distinct tip states of 'individual atoms' (for the sharpest tips), 'dimers, 'asymmetries', and 'rows' have been observed and discussed in the literature [18,22,23]. Typically, an operator will want to coerce the tip into producing images with one of these atomistic resolutions visible.…”

Section: H:si(100) Datasetmentioning

confidence: 99%

Embedding human heuristics in machine-learning-enabled probe microscopy

Gordon

Junqueira

Moriarty

2020

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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.

show abstract

Section: H:si(100) Datasetmentioning

confidence: 99%

Embedding human heuristics in machine-learning-enabled probe microscopy

Gordon

Junqueira

Moriarty

2020

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Whilst these particular features can be seen when scanning any surface, others are specific to the surface being investigated [18]. For example, for the H:Si(100) surface, four different, distinct tip states of 'individual atoms' (for the sharpest tips), 'dimers, 'asymmetries', and 'rows' have been observed and discussed in the literature [15,19,20]. Typically, an operator will want to coerce the tip into producing images with one of these atomistic resolutions visible.…”

Section: H:si(100) Datasetmentioning

confidence: 99%

Embedding Human Heuristics in Machine-Learning-Enabled Probe Microscopy

Gordon,

Junqueira,

Moriarty

2019

Preprint

View full text Add to dashboard Cite

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 triedand-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.

show abstract

“…The key for the correction of DB structures is the control of the tip functionalization. It has been shown before that the final tip atom is of utmost importance for AFM imaging with atomic resolution [30][31][32][33][34] and the importance of the tip for hydrogen lithography has been pointed out 25 . On hydrogenated Si surfaces two distinctly different tip functionalizations have been achieved, characterized and assigned using the combination of STM and AFM [31][32][33][34] .…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown before that the final tip atom is of utmost importance for AFM imaging with atomic resolution [30][31][32][33][34] and the importance of the tip for hydrogen lithography has been pointed out 25 . On hydrogenated Si surfaces two distinctly different tip functionalizations have been achieved, characterized and assigned using the combination of STM and AFM [31][32][33][34] . On the one hand a chemically inert tip was assigned as an H tip 31,34 , and on the other hand a highly reactive tip was assigned as a Si tip 33 , the latter enabling even room-temperature atomic-resolution imaging of organic molecules by AFM 35 .…”

Section: Introductionmentioning

confidence: 99%

“…On hydrogenated Si surfaces two distinctly different tip functionalizations have been achieved, characterized and assigned using the combination of STM and AFM [31][32][33][34] . On the one hand a chemically inert tip was assigned as an H tip 31,34 , and on the other hand a highly reactive tip was assigned as a Si tip 33 , the latter enabling even room-temperature atomic-resolution imaging of organic molecules by AFM 35 . We achieved writing and correcting Si DBs by employing these two different tip functionalizations: a Si tip is used to write DBs, that is for hydrogen desorption, and a H tip is used for error correction, that is for hydrogen passivation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Passivation of dangling bonds on hydrogenated Si(100)-2$\times$1: a possible method for error correction in hydrogen lithography

Pavliček,

Majzik,

Meyer

et al. 2017

Preprint

View full text Add to dashboard Cite

Using combined low temperature scanning tunneling microscopy (STM) and atomic force microscopy (AFM), we demonstrate hydrogen passivation of individual, selected dangling bonds (DBs) on a hydrogen-passivated Si(100)-2×1 surface (H-Si) by atom manipulation. This method allows erasing of DBs and thus provides an error-correction scheme for hydrogen lithography. Si-terminated tips (Si tips) for hydrogen desorption and H-terminated tips (H tips) for hydrogen passivation are both created by deliberate contact to the H-Si surface and are assigned by their characteristic contrast in AFM. DB passivation is achieved by transferring the H atom that is at the apex of an H tip to the DB, reestablishing a locally defect-free H-Si surface.

show abstract

Measuring the reactivity of a silicon-terminated probe

Cited by 8 publications

References 38 publications

Embedding human heuristics in machine-learning-enabled probe microscopy

Embedding human heuristics in machine-learning-enabled probe microscopy

Embedding Human Heuristics in Machine-Learning-Enabled Probe Microscopy

Passivation of dangling bonds on hydrogenated Si(100)-2$\times$1: a possible method for error correction in hydrogen lithography

Contact Info

Product

Resources

About