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Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks
Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networ...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561110/ https://www.ncbi.nlm.nih.gov/pubmed/36229465 http://dx.doi.org/10.1038/s41467-022-33877-7 |
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author | Shi, Yang Ren, Junyu Chen, Guanyu Liu, Wei Jin, Chuqi Guo, Xiangyu Yu, Yu Zhang, Xinliang |
author_facet | Shi, Yang Ren, Junyu Chen, Guanyu Liu, Wei Jin, Chuqi Guo, Xiangyu Yu, Yu Zhang, Xinliang |
author_sort | Shi, Yang |
collection | PubMed |
description | Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networks. Here, we highlight nonlinear germanium-silicon photodiodes to construct on-chip optical neurons and a self-monitored all-optical neural network. With specifically engineered optical-to-optical and optical-to-electrical responses, the proposed neuron merges the all-optical activation and non-intrusive monitoring functions in a compact footprint of 4.3 × 8 μm(2). Experimentally, a scalable three-layer photonic neural network enables in situ training and learning in object classification and semantic segmentation tasks. The performance of this neuron implemented in a deep-scale neural network is further confirmed via handwriting recognition, achieving a high accuracy of 97.3%. We believe this work will enable future large-scale photonic intelligent processors with more functionalities but simplified architecture. |
format | Online Article Text |
id | pubmed-9561110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-95611102022-10-15 Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks Shi, Yang Ren, Junyu Chen, Guanyu Liu, Wei Jin, Chuqi Guo, Xiangyu Yu, Yu Zhang, Xinliang Nat Commun Article Silicon photonics is promising for artificial neural networks computing owing to its superior interconnect bandwidth, low energy consumption and scalable fabrication. However, the lack of silicon-integrated and monitorable optical neurons limits its revolution in large-scale artificial neural networks. Here, we highlight nonlinear germanium-silicon photodiodes to construct on-chip optical neurons and a self-monitored all-optical neural network. With specifically engineered optical-to-optical and optical-to-electrical responses, the proposed neuron merges the all-optical activation and non-intrusive monitoring functions in a compact footprint of 4.3 × 8 μm(2). Experimentally, a scalable three-layer photonic neural network enables in situ training and learning in object classification and semantic segmentation tasks. The performance of this neuron implemented in a deep-scale neural network is further confirmed via handwriting recognition, achieving a high accuracy of 97.3%. We believe this work will enable future large-scale photonic intelligent processors with more functionalities but simplified architecture. Nature Publishing Group UK 2022-10-13 /pmc/articles/PMC9561110/ /pubmed/36229465 http://dx.doi.org/10.1038/s41467-022-33877-7 Text en © The Author(s) 2022 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 Shi, Yang Ren, Junyu Chen, Guanyu Liu, Wei Jin, Chuqi Guo, Xiangyu Yu, Yu Zhang, Xinliang Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title | Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title_full | Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title_fullStr | Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title_full_unstemmed | Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title_short | Nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
title_sort | nonlinear germanium-silicon photodiode for activation and monitoring in photonic neuromorphic networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561110/ https://www.ncbi.nlm.nih.gov/pubmed/36229465 http://dx.doi.org/10.1038/s41467-022-33877-7 |
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