scholarly journals Enabling Autonomous Scanning Probe Microscopy Imaging of Single Molecules with Deep Learning

Nanoscale ◽  
2021 ◽  
Author(s):  
Javier Sotres ◽  
Hannah Boyd ◽  
Juan Francisco Gonzalez-Martinez
Keyword(s):  
Deep Learning ◽  
Scanning Probe Microscopy ◽  
Signal To Noise Ratio ◽  
Single Molecules ◽  
Real Space ◽  
Scanning Probe ◽  
Signal To Noise ◽  
Microscopy Imaging ◽  
Probe Microscopy ◽  
The Real

Scanning Probe Microscopies allow investigating surfaces at the nanoscale, in the real space and with unparalleled signal-to-noise ratio. However, these microscopies are not used as much as it would be...

Lateral Resolution and Signal to Noise Ratio in Electrostatic Force Detection Based on Scanning Probe Microscopy

2012 ◽  
Vol 29 (7) ◽  
pp. 070703 ◽  
Author(s):  
Dong-Dong Zhang ◽  
Xiao-Wei Wang ◽  
Rui Wang ◽  
Sheng-Nan Wang ◽  
Zhi-Hai Cheng ◽  
...  
Keyword(s):  
Scanning Probe Microscopy ◽  
Signal To Noise Ratio ◽  
Electrostatic Force ◽  
Scanning Probe ◽  
Lateral Resolution ◽  
Signal To Noise ◽  
Force Detection ◽  
Probe Microscopy ◽  
Noise Ratio

scholarly journals Scanning probe microscopy imaging of metallic nanocontacts

2012 ◽  
Vol 85 (3) ◽  
Author(s):  
D. Stöffler ◽  
S. Fostner ◽  
P. Grütter ◽  
R. Hoffmann-Vogel
Keyword(s):  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Microscopy Imaging ◽  
Probe Microscopy

Real-space observation on standing configurations of phenylacetylene on Cu (111) by scanning probe microscopy

2019 ◽  
Vol 28 (6) ◽  
pp. 066801
Author(s):  
Jing Qi ◽  
Yi-Xuan Gao ◽  
Li Huang ◽  
Xiao Lin ◽  
Jia-Jia Dong ◽  
...  
Keyword(s):  
Scanning Probe Microscopy ◽  
Real Space ◽  
Scanning Probe ◽  
Probe Microscopy

scholarly journals The role of convolutional neural networks in scanning probe microscopy: a review

2021 ◽  
Vol 12 ◽  
pp. 878-901
Author(s):  
Ido Azuri ◽  
Irit Rosenhek-Goldian ◽  
Neta Regev-Rudzki ◽  
Georg Fantner ◽  
Sidney R Cohen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Data Set ◽  
Probe Microscopy ◽  
Instrumental Control

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

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