Broadband on-chip spectrometer enabled by machine learning methods

We present a novel machine learning-based approach for high-accuracy broadband spectrum reconstruction in on-chip random spectrometers. Furthermore, we generalize the concept of random spectrometers using random matrix theory and introduce a new spectrometer design that offers a smaller footprint while maintaining equivalent performance.