Journal ArticleDOI
Double-Tip Artifact Removal From Atomic Force Microscopy Images
Yun-feng Wang,Jason I. Kilpatrick,Suzanne P. Jarvis,Francis M. Boland,Anil Kokaram,David Corrigan +5 more
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TLDR
This work applies a novel deblurring technique, using a Bayesian framework, to yield a reliable estimation of the real surface topography without any prior knowledge of the tip geometry (blind reconstruction), and focuses specifically on the double-tip effect.Abstract:
The atomic force microscopy (AFM) allows the measurement of interactions at interfaces with nanoscale resolution. Imperfections in the shape of the tip often lead to the presence of imaging artifacts, such as the blurring and repetition of objects within images. In general, these artifacts can only be avoided by discarding data and replacing the probe. Under certain circumstances (e.g., rare, high-value samples, or extensive chemical/physical tip modification), such an approach is not feasible. Here, we apply a novel deblurring technique, using a Bayesian framework, to yield a reliable estimation of the real surface topography without any prior knowledge of the tip geometry (blind reconstruction). A key contribution is to leverage the significant recently successful body of work in natural image deblurring to solve this problem. We focus specifically on the double-tip effect, where two asperities1 are present on the tip, each contributing to the image formation mechanism. Finally, we demonstrate that the proposed technique successfully removes the double-tip effect from high-resolution AFM images, which demonstrate this artifact while preserving feature resolution.1
An asperity is a localized sharp peak in the surface of an object.read more
Citations
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Journal ArticleDOI
Scanning tunneling state recognition with multi-class neural network ensembles
Oliver M. Gordon,P. D'Hondt,L. Knijff,S. E. Freeney,Filipe L. Q. Junqueira,Philip Moriarty,Ingmar Swart +6 more
TL;DR: A convolutional neural network protocol is introduced that enables automated recognition of a variety of desirable and undesirable scanning tunneling tip states on both metal and nonmetal surfaces by combining the best performing models into majority voting ensembles.
Journal ArticleDOI
Scanning Probe State Recognition With Multi-Class Neural Network Ensembles.
Oliver M. Gordon,P. D'Hondt,L. Knijff,S. E. Freeney,Filipe L. Q. Junqueira,Philip Moriarty,Ingmar Swart +6 more
TL;DR: In this paper, a convolutional neural network (CNN) was used to identify desirable and undesirable scanning probe tip states on both metal and non-metal surfaces. But the results were limited to the detection of H:Si(100) tip states.
Journal ArticleDOI
Embedding human heuristics in machine-learning-enabled probe microscopy
TL;DR: Various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans are explored and a protocol to detect the state of the tip apex in real time is introduced.
Journal ArticleDOI
Effective method to simultaneously release residual stress and promote planarization of surface indentation achieved by secondary indentation
TL;DR: In this paper, a novel method was proposed to effectively release residual stress and promote surface planarization, which included an initiation indentation by using Vickers indenter and a secondary indentation at nano-scale on already formed surface by using a cube-corner indenter.
Journal ArticleDOI
Embedding Human Heuristics in Machine-Learning-Enabled Probe Microscopy
TL;DR: In this paper, the authors explore various strategies by which different STM image classes (arising from changes in the tip state) can be correctly identified from partial scans using a secondary temporal network and a rolling window of a small group of individual scanlines.
References
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
Atomic force microscope
TL;DR: The atomic force microscope as mentioned in this paper is a combination of the principles of the scanning tunneling microscope and the stylus profilometer, which was proposed as a method to measure forces as small as 10-18 N. As one application for this concept, they introduce a new type of microscope capable of investigating surfaces of insulators on an atomic scale.
Journal ArticleDOI
On the statistical analysis of dirty pictures
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
Journal ArticleDOI
Gwyddion: an open-source software for SPM data analysis
David Nečas,Petr Klapetek +1 more
TL;DR: It is shown that on the basis of open-source software development, a fully functional software package can be created that covers the needs of a large part of the scanning probe microscopy user community.
Journal ArticleDOI
Removing camera shake from a single photograph
TL;DR: This work introduces a method to remove the effects of camera shake from seriously blurred images, which assumes a uniform camera blur over the image and negligible in-plane camera rotation.
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