Spectral kernel machines with electrically tunable photodetectors

Spectral machine vision collects spectral and spatial information as dense
3D hypercubes and digitally processes them into scene recognition, which
causes a data bottleneck, limiting power efficiency, frame rate, and
spectral-spatial resolution. This work introduces a device architecture
called spectral kernel machines (SKM) to overcome these bottlenecks. SKM
directly compresses spectral analysis through the output photocurrent and
learns from example objects to identify and classify new samples in a
'sniff-and-seek' mode. We experimentally demonstrated SKM with
electrically tunable bipolar black phosphorous (bP)-MoS2 photodiodes in
the near/mid-infrared band and silicon photoconductors in the visible
band, performing versatile intelligent tasks from chemometrics to
semiconductor metrology. This architecture consumes significantly lower
power and is more than an order of magnitude faster than existing
solutions for hyperspectral image analysis, defining an intelligent
imaging and sensing paradigm with intriguing possibilities.