Deep learning-based spectral reconstruction on a chip using a scalable plasmonic encoder

We demonstrate a deep learning-based spectroscopy framework using a low-cost on-chip plasmonic encoder. When blindly tested on N=14,648 random spectra our system shows competitive performance, where the reconstruction of an unknown spectrum on average takes similar to 28 mu s. (c) 2021 The Author(s)