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In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity
Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the desig...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352275/ https://www.ncbi.nlm.nih.gov/pubmed/37460605 http://dx.doi.org/10.1038/s41467-023-40055-w |
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author | Kwak, Dohyun Polyushkin, Dmitry K. Mueller, Thomas |
author_facet | Kwak, Dohyun Polyushkin, Dmitry K. Mueller, Thomas |
author_sort | Kwak, Dohyun |
collection | PubMed |
description | Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the design and realization of a smart photodetector that provides such information directly without the need to explicitly record a spectrum. This is achieved by tailoring the spectral responsivity of the device to a specific purpose. In-sensor computation is performed at the lowest possible level of the sensor system hierarchy – the physical level of photon detection – and does not require any external processing of the measurement data. The device can be programmed to cover different types of spectral regression or classification tasks. We present the analysis of spectral mixtures as an example, but the scheme can also be applied to any other algorithm that can be represented by a linear operator. Our prototype physical implementation utilizes an ensemble of optical cavity-enhanced MoS(2) photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing. |
format | Online Article Text |
id | pubmed-10352275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-103522752023-07-19 In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity Kwak, Dohyun Polyushkin, Dmitry K. Mueller, Thomas Nat Commun Article Optical spectroscopy is an indispensable technique in almost all areas of scientific research and industrial applications. After its acquisition, an optical spectrum is usually further processed using a mathematical algorithm to classify or quantify the measurement results. Here we present the design and realization of a smart photodetector that provides such information directly without the need to explicitly record a spectrum. This is achieved by tailoring the spectral responsivity of the device to a specific purpose. In-sensor computation is performed at the lowest possible level of the sensor system hierarchy – the physical level of photon detection – and does not require any external processing of the measurement data. The device can be programmed to cover different types of spectral regression or classification tasks. We present the analysis of spectral mixtures as an example, but the scheme can also be applied to any other algorithm that can be represented by a linear operator. Our prototype physical implementation utilizes an ensemble of optical cavity-enhanced MoS(2) photodetectors with different center wavelengths and individually adjustable peak responsivities. This spectroscopy method represents a significant advance in miniaturized and energy-efficient optical sensing. Nature Publishing Group UK 2023-07-17 /pmc/articles/PMC10352275/ /pubmed/37460605 http://dx.doi.org/10.1038/s41467-023-40055-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Kwak, Dohyun Polyushkin, Dmitry K. Mueller, Thomas In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title | In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title_full | In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title_fullStr | In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title_full_unstemmed | In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title_short | In-sensor computing using a MoS(2) photodetector with programmable spectral responsivity |
title_sort | in-sensor computing using a mos(2) photodetector with programmable spectral responsivity |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10352275/ https://www.ncbi.nlm.nih.gov/pubmed/37460605 http://dx.doi.org/10.1038/s41467-023-40055-w |
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