RESUMEN
Waveguide-enhanced Raman spectroscopy (WERS) is a promising technique for sensitive and selective detection of chemicals in a compact chip-scale platform. Coupling light on and off the sensor chip with fibers however presents challenges because of the fluorescence and Raman background generated by the pump light in the fibers; as a result all WERS demonstrations to date have used free-space coupling via lenses. We report a packaged, fiber-bonded WERS chip that filters the background on-chip through collection of the backscattered Raman light. The packaged sensor is integrated in a ruggedized flow cell for reliable measurement over arbitrary time periods. We also derive the figures of merit for WERS sensing with the backscattered Raman signal and compare waveguide geometries with respect to their filtering performance and signal to noise ratio.
RESUMEN
Low-loss waveguides constitute an important building block for integrated photonic systems. In this work, we investigated low-loss photonic device fabrication in Ge23Sb7S70 chalcogenide glass using electron beam lithography followed by plasma dry etching. High-index-contrast waveguides with a low propagation loss of 0.5 dB/cm and microdisk resonators with an intrinsic quality factor (Q-factor) of 1.2×106 were demonstrated. Both figures represent, to the best of our knowledge, the best low-loss results reported thus far in submicrometer single-mode chalcogenide glass devices.
RESUMEN
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-D1' regularized regression method that we develop, 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.