Neural Network-Based On-Chip Spectroscopy Using a Scalable Plasmonic Encoder

• Published in: ACS nano Vol. 15; no. 4; pp. 6305 - 6315
• Main Authors: Brown, Calvin, Goncharov, Artem, Ballard, Zachary S, Fordham, Mason, Clemens, Ashley, Qiu, Yunzhe, Rivenson, Yair, Ozcan, Aydogan
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27.04.2021
• Subjects: on-chip spectroscopy; deep learning; computational spectroscopy; plasmonics; neural networks
• ISSN: 1936-0851; 1936-086X; 1936-086X
• DOI: 10.1021/acsnano.1c00079

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 many of 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 specific geometry and thus a specific 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, lightweight, 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 noniterative manner. Benefiting from the parallelization of neural networks, the average inference time per spectrum is ∼28 μs, which is much faster compared to other computational spectroscopy approaches. When blindly tested on 14 648 unseen spectra 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.