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• W3126861281 endingPage "6315" @default.
• W3126861281 startingPage "6305" @default.
• W3126861281 abstract "Conventional spectrometers are limited by trade-offs set by size, cost, signal-to-noise ratio (SNR), and spectral resolution. Here, we demonstrate a deep learning-based spectral reconstruction framework, using a compact and low-cost on-chip sensing scheme that is not constrained by the design trade-offs inherent to grating-based spectroscopy. The system employs a plasmonic spectral encoder chip containing 252 different tiles of nanohole arrays fabricated using a scalable and low-cost imprint lithography method, where each tile has a unique geometry and, thus, a unique optical transmission spectrum. The illumination spectrum of interest directly impinges upon the plasmonic encoder, and a CMOS image sensor captures the transmitted light, without any lenses, gratings, or other optical components in between, making the entire hardware highly compact, light-weight and field-portable. A trained neural network then reconstructs the unknown spectrum using the transmitted intensity information from the spectral encoder in a feed-forward and non-iterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ~28 microseconds, which is orders of magnitude faster compared to other computational spectroscopy approaches. When blindly tested on unseen new spectra (N = 14,648) with varying complexity, our deep-learning based system identified 96.86% of the spectral peaks with an average peak localization error, bandwidth error, and height error of 0.19 nm, 0.18 nm, and 7.60%, respectively. This system is also highly tolerant to fabrication defects that may arise during the imprint lithography process, which further makes it ideal for applications that demand cost-effective, field-portable and sensitive high-resolution spectroscopy tools." @default.
• W3126861281 created "2021-02-15" @default.
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• W3126861281 date "2021-02-05" @default.
• W3126861281 modified "2023-10-16" @default.
• W3126861281 title "Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder" @default.
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• W3126861281 doi "https://doi.org/10.1021/acsnano.1c00079" @default.
• W3126861281 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33543919" @default.
• W3126861281 hasPublicationYear "2021" @default.
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