  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.
