Silicon microspectrometer chip based on nanostructured fishnet photodetectors with tailored responsivities and machine learning

Abstract

The realization of on-chip microspectrometers would allow spectroscopy and colorimetry measurement systems to be readily incorporated into platforms for which size and weight are critical, such as consumer grade electronics, smartphones, and unmanned aerial vehicles. This would allow them to find use in diverse fields such as interior design, agriculture, and in machine vision applications. All spectrometers require a detector or detector array and optical elements for spectral discrimination. A single device that combines both detection and spectral discrimination functions therefore represents an ultimate limit of miniaturization. Motivated by this, we here experimentally demonstrate a novel nanostructured silicon-based photodetector design whose responsivity can be tailored by an appropriate choice of geometric parameters. We utilize a unique doping profile with two vertically stacked, back-to-back photodiode regions, which allows us to double the number of detectors in a given on-chip footprint. By patterning the top photosensitive regions of each device with two sets of interleaved vertical slab waveguide arrays of varied width and period, we define the absorption spectra (and thus responsivity spectra) of both the upper and lower photodiode regions. We then use twenty such “fishnet pixels” to form a microspectrometer chip and demonstrate the reconstruction of four test spectra using a two-stage supervised machine-learning-based reconstruction algorithm.