Neural network-based on-chip spectroscopy using a scalable plasmonic encoder

• URL: http://arxiv.org/abs/2012.00878v1
• Date: Tue, 1 Dec 2020 22:50:06 GMT
• Title: Neural network-based on-chip spectroscopy using a scalable plasmonic encoder
• Authors: Calvin Brown, Artem Goncharov, Zachary Ballard, Mason Fordham, Ashley Clemens, Yunzhe Qiu, Yair Rivenson and Aydogan Ozcan
• Abstract summary: 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.
• Score: 0.4397520291340694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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