Submonolayer InGaAs/GaAs Quantum Dots Grown by MOCVD

Aleshkin, V. Ya.; Baidus, N. V.; Dubinov, A. A.; Kudryavtsev, K. E.; Nekorkin, S. M.; Круглов, А. В.; Reunov, D. G.

doi:10.1134/s1063782619080037

Cited by 5 publications

(1 citation statement)

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“…As a rule, calculating TOF requires processing data on hundreds of particles from several points of the catalyst, the more the better. A similar task arises for researchers in the synthesis and characterization of quantum dots [ 3 , 4 , 5 , 6 ]. Software products, widely used in probe methods, including scanning tunneling microscopy, such as WSxM [ 7 ], Gwyddion [ 8 ], allow the operator to measure particle parameters manually.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle Recognition on Scanning Probe Microscopy Images Using Computer Vision and Deep Learning

et al. 2020

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Identifying, counting and measuring particles is an important component of many research studies. Images with particles are usually processed by hand using a software ruler. Automated processing, based on conventional image processing methods (edge detection, segmentation, etc.) are not universal, can only be used on good-quality images and need to set a number of parameters empirically. In this paper, we present results from the application of deep learning to automated recognition of metal nanoparticles deposited on highly oriented pyrolytic graphite on images obtained by scanning tunneling microscopy (STM). We used the Cascade Mask-RCNN neural network. Training was performed on a dataset containing 23 STM images with 5157 nanoparticles. Three images containing 695 nanoparticles were used for verification. As a result, the trained neural network recognized nanoparticles in the verification set with 0.93 precision and 0.78 recall. Predicted contour refining with 2D Gaussian function was a proposed option. The accuracies for mean particle size calculated from predicted contours compared with ground truth were in the range of 0.87–0.99. The results were compared with outcomes from other generally available software, based on conventional image processing methods. The advantages of deep learning methods for automatic particle recognition were clearly demonstrated. We developed a free open-access web service “ParticlesNN” based on the trained neural network, which can be used by any researcher in the world.

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