Chip-scale high-performance digital Fourier Transform (dFT) spectrometers

On-chip spectrometers have recently emerged as a promising alternative to conventional benchtop instruments with apparent Size, Weight, and Power (SWaP) advantages for applications including spectroscopic sensing, optical network performance monitoring, RF spectrum analysis, optical coherence tomography, and hyperspectral imaging. Existing on chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio (SNR). Here we demonstrate a transformative on-chip digital Fourier transform (dFT) spectrometer that can acquire high-resolution spectra via time-domain modulation of a reconfigurable Mach-Zehnder interferometer. The device, fabricated and packaged using industry-standard silicon photonics technology, claims the multiplex advantage to dramatically boost SNR and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explored and implemented machine learning regularization techniques to spectrum reconstruction. Using an `elastic-Di' regularized regression method that we developed, we achieved significant noise suppression for both broad (> 600 GHz) and narrow (< 25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion. The dFT architecture and spectrum reconstruction techniques demonstrated in this work will drive future work in on-chip optical spectroscopy and enable practical realizations of high-performance chip-scale spectrometers with large (> 1,000) spectral channel counts.