Computational spectrometers enabled by nanophotonics and deep learning

Abstract

A new type of spectrometer that heavily relies on computational technique to recover spectral information is introduced. They are different from conventional optical spectrometers in many important aspects. Traditional spectrometers offer high spectral resolution and wide spectral range, but they are so bulky and expensive as to be difficult to deploy broadly in the field. Emerging applications in machine sensing and imaging require low-cost miniaturized spectrometers that are specifically designed for certain applications. Computational spectrometers are well suited for these applications. They are generally low in cost and offer single-shot operation, with adequate spectral and spatial resolution. The new type of spectrometer combines recent progress in nanophotonics, advanced signal processing and machine learning. Here we review the recent progress in computational spectrometers, identify key challenges, and note new directions likely to develop in the near future.

Keywords

• compressive sensing
• deep learning
• nanophotonics
• spectral imaging
• spectral sensing
• spectroscopy