High-performance and scalable on-chip digital Fourier transform spectroscopy

TLDR

On‑chip spectrometers promise dramatic size, weight, and power advantages over benchtop instruments for applications such as sensing, network monitoring, hyperspectral imaging, and RF spectrum analysis, yet current designs are limited by spectral channel count and signal‑to‑noise ratio. The study demonstrates a transformative on‑chip digital Fourier transform spectrometer that acquires high‑resolution spectra via time‑domain modulation of a reconfigurable Mach‑Zehnder interferometer. The device uses a reconfigurable Mach‑Zehnder interferometer modulated in time and applies machine‑learning regularization, specifically an elastic‑D1 regression, to reconstruct spectra. The silicon‑photonic device achieves a multiplex advantage that dramatically boosts signal‑to‑noise ratio and scalability, and the elastic‑D1 regularized regression yields significant noise suppression for both broad (>600 GHz) and narrow (<25 GHz) features while surpassing the classical Rayleigh resolution limit.

Abstract

Abstract On-chip spectrometers have the potential to offer dramatic size, weight, and power advantages over conventional benchtop instruments for many applications such as spectroscopic sensing, optical network performance monitoring, hyperspectral imaging, and radio-frequency spectrum analysis. Existing on-chip spectrometer designs, however, are limited in spectral channel count and signal-to-noise ratio. Here we demonstrate a transformative on-chip digital Fourier transform spectrometer that acquires 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 the signal-to-noise ratio and unprecedented scalability capable of addressing exponentially increasing numbers of spectral channels. We further explore and implement machine learning regularization techniques to spectrum reconstruction. Using an ‘elastic-D 1 ’ regularized regression method that we develop, we achieved significant noise suppression for both broad (&gt;600 GHz) and narrow (&lt;25 GHz) spectral features, as well as spectral resolution enhancement beyond the classical Rayleigh criterion.