Reconstruction of atomic force microscopy image using compressed sensing

Han, Guoqiang; Lin, Bo; Lin, Yuling

doi:10.1016/j.micron.2017.11.003

Cited by 13 publications

(6 citation statements)

References 34 publications

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“…As such, the broad adoption of non‐rectangular scans [ 34–40 ] necessitates the development of the algorithmic tools that allow conversion of the data stream acquired along the fixed or dynamically adjusted probe path on the classical rectangular grids. It should be noted that most of the non‐rectangular scans were motivated by the need to accelerate the scanning speed and were generally either spiral or Lissajous scans.…”

Section: Introductionmentioning

confidence: 99%

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Kelley

Ziatdinov

Collins

et al. 2020

Small

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Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. This study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

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Section: Introductionmentioning

confidence: 99%

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Kelley

Ziatdinov

Collins

et al. 2020

Small

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“…The total engagement-withdrawal time was reduced by over 61%, from ~5.4s to ~2.1s. The improvements in both the engagement-withdrawal time and the interaction force became more pronounced, where a large number of probe engagement-withdrawals are involved [9,12,26]. Therefore, the rapid probe engagement and withdrawal in the RB-DNM approach could be an effective tool for AFM applications.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the rapid and (a-i) The measured and filtered complex modulus of the location A to I measured in the first iteration of the forward mapping using the RB-DNM approach on the PDMS-epoxy sample, respectively. precise between-location transition in the RB-DNM approach is promising to improve the efficiency and accuracy of AFM imaging/mapping applications with frequent probe transitions [9,12,26].…”

Section: Discussionmentioning

confidence: 99%

Rapid broadband discrete nanomechanical mapping of soft samples on atomic force microscope

Wang

Zou

et al. 2020

Nanotechnology

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In this paper, an approach to achieve rapid broadband discrete nanomechanical mapping of soft samples using an atomic force microscope is developed. Nanomechanical mapping (NM) is needed to investigate, for example, dynamic evolution of the nanomechanical distribution of the sample—provided that the mapping is fast enough. The throughput of conventional NM methods, however, is inherently limited by the continuous scanning involved where the probe visits each sampling location continuously. Thus, we propose to significantly reduce the number of measurements through discrete mapping where only discrete sampling locations of interests are visited and measured. An online-searching learning-based technique is utilized to achieve rapid probe engagement and withdrawal with the interaction force minimized at each sampling location. Then, a control-based nanoindentation measurement technique is used to quickly acquire the nanomechanical property at each location, over frequencies that can be chosen arbitrarily in a broad range. Finally, a decomposition-based learning approach is explored to achieve rapid probe transitions between the sampling locations. The proposed technique is demonstrated through experiments using a Polydimethylsiloxane (PDMS) sample and a PDMS-epoxy sample as examples.

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“…provides non-destructive surface characterization at the nanoscale, accessing the full profile topography of 3D nanostructures is not easy due to the geometric constraints of the pyramid tips of commercial standard AFM probes. For example, to detect the sidewall or bottom texturing of deep trenches, a promising solution to this challenge is using high-aspect-ratio (HAR)-AFM probes [11][12][13][14][15][16][17][18][19][20]. Compared with optical microscopes, the working principle of point-by-point scanning of AFM leads to a severe limitation of its imaging speed.…”

Section: Introductionmentioning

confidence: 99%

Design and realization of scanning probe microscope based on a T-shaped high-aspect-ratio probe

Xu,

Liu,

Ren

et al. 2023

Meas. Sci. Technol.

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With the growing importance of three-dimensional (3D) nanomaterials and devices, the use of high-aspect-ratio (HAR) probes in atomic force microscopy (AFM) imaging enables full-profile topography characterization. However, HAR-AFM probes are difficult to manufacture due to the expensive materials and complex processes. Inspired by the Wollaston scanning probe, we successfully fabricated a T-shaped tungsten probe and installed in a home-built scanning probe microscope (SPM) system. Compared with commercial AFM, the self-developed platform utilizes a set of LabVIEW control programs based on Field Programmable Gate Array (FPGA) hardware feedback, which improves the scanning speed and makes the control program more flexible. The reliability of the system was verified by comparing with the scanning results of commercial AFM. The T-shaped probe exhibits better fidelity in HAR-hole imaging compared to AFM standard pyramidal probes. This approach will likely find a convenient and material-saving fabrication route, especially for custom functional nanoprobes.

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Reconstruction of atomic force microscopy image using compressed sensing

Cited by 13 publications

References 34 publications

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast Scanning Probe Microscopy via Machine Learning: Non‐Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Rapid broadband discrete nanomechanical mapping of soft samples on atomic force microscope

Design and realization of scanning probe microscope based on a T-shaped high-aspect-ratio probe

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