Automatic 2D material detection in optical images using deep-learning-based computer vision

Fereshteh Ramezani; Sheikh Parvez; J. Pierce Fix; Arthur Battaglin; Seamus Whyte; Nicholas J. Borys; Bradley Whitaker

doi:10.1117/12.2647425

15 March 2023 Automatic 2D material detection in optical images using deep-learning-based computer vision

Fereshteh Ramezani, Sheikh Parvez, J. Pierce Fix, Arthur Battaglin, Seamus Whyte, Nicholas J. Borys, Bradley Whitaker

Proceedings Volume 12438, AI and Optical Data Sciences IV; 1243816 (2023) https://doi.org/10.1117/12.2647425
Event: SPIE OPTO, 2023, San Francisco, California, United States

Conference Poster

Abstract

Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. In order to use these materials for research and product development, single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of 2D materials based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectorRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes (< 50 atomic layers thick).

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Fereshteh Ramezani, Sheikh Parvez, J. Pierce Fix, Arthur Battaglin, Seamus Whyte, Nicholas J. Borys, and Bradley Whitaker "Automatic 2D material detection in optical images using deep-learning-based computer vision", Proc. SPIE 12438, AI and Optical Data Sciences IV, 1243816 (15 March 2023); https://doi.org/10.1117/12.2647425

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