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Automated system for the detection of 2D materials using digital image processing and deep learning
| dc.contributor.author | Sanchez-Juarez, Jesús | |
| dc.contributor.author | Granados-Baez, Marissa | |
| dc.contributor.author | Aguilar Lasserre, Alberto Alfonso | |
| dc.contributor.author | Cárdenas, Jaime | |
| dc.date.accessioned | 2022-08-09T13:48:28Z | |
| dc.date.available | 2022-08-09T13:48:28Z | |
| dc.date.issued | 2022-04-06 | |
| dc.identifier.citation | https://doi.org/10.1364/OME.454314 | es |
| dc.identifier.issn | 2159-3930 | |
| dc.identifier.uri | http://repositorios.orizaba.tecnm.mx:8080/xmlui/handle/123456789/673 | |
| dc.description | Patrocinó el CONACYT a través del Programa Nacional de Becas y la Universidad de Rochester por medio del financiamiento de estancia en el Instituto de Óptica. | es |
| dc.description.abstract | The unique properties of two-dimensional materials for light emission, detection, and modulation make them ideal for integrated photonic devices. However, identifying if the films are indeed monolayers is a time-consuming process even for well-trained operators. We develop an intelligent algorithm to detect monolayers of WSe2, MoS2 and h-BN autonomously using Digital Image Processing and Deep Learning with high accuracy rate, avoiding human interaction and any additional characterization tests. We demonstrate an autonomous detection algorithm for TMDC’s and h-BN monolayers with high accuracy of 99.9% with a total processing time of 9 minutes per 1cm2. . | es |
| dc.description.sponsorship | CONACYT y Universidad de Rochester | es |
| dc.language.iso | es | es |
| dc.publisher | Optica Publishing Group | es |
| dc.relation.ispartofseries | 12;5 | |
| dc.subject | Automated system | es |
| dc.subject | Detection of 2D materials | es |
| dc.subject | Digital image processing | es |
| dc.subject | Deep learning | es |
| dc.title | Automated system for the detection of 2D materials using digital image processing and deep learning | es |
| dc.type | Article | es |
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