Deep Learning Takes 2D Material Microscopy Images Further: AdvSciNews 2DMaterials MachineLearning DeepLearning Microscopy nanotechnology nanomaterials
By Bhavna KavetiJul 19 2022Reviewed by Susha Cheriyedath, M.Sc. The reduced dimensionality and heterostructures of 2D materials make them promising candidates for the fabrication of photonic and optical devices. The electrical, mechanical, and optical properties of 2D materials depend on their layered structure.
Later, an evaluation of images with 2D microscopy with different optical contrast variations augmented the images to determine their robustness. Furthermore, the deep learning models were optimized and evaluated for the identification of mono- to multilayered molybdenum sulfide flakes grown on silica/silicon substrate via chemical vapor deposition .
Related StoriesOf the mentioned techniques, the optical contrast method involves the manual observation of 2D materials under a microscope, which is a time-delayed process. However, the optical contrast’s identification of atomic layer numbers from a computer vision perspective involves classification, segmentation, and detection steps for 2D materials image processing tasks.
Previous attempts of network training with 2D materials collected data helped identify their rough thickness without using the datasets. However, the same datasets can be used to study different network architectures systematically to investigate the correlation between optical contrast variations and network accuracy.
The complexity of the datasets increased due to optical contrast variations, making it difficult to distinguish among categories of 2D materials. However, processing the 2D materials microscopic images for the three deep learning approaches provided the solution for 2D materials identification. Furthermore, statistics and model performances of datasets were analyzed by employing red, green, and blue histograms of optical contrast differences and commission on illumination 1931 color space.
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