Development of Photonic In-Sensor Computing Based on a Mid-Infrared Silicon Waveguide Platform.

Neuromorphic in-sensor computing has provided an energy-efficient solution to smart sensor design and on-chip data processing. In recent years, various free-space-configured optoelectronic chips have been demonstrated for on-chip neuromorphic vision processing. However, on-chip waveguide-based in-sensor computing with different data modalities is still lacking. Here, by integrating a responsivity-tunable graphene photodetector onto the silicon waveguide, an on-chip waveguide-based in-sensor processing unit is realized in the mid-infrared wavelength range. The weighting operation is achieved by dynamically tuning the bias of the photodetector, which could reach 4 bit weighting precision. Three different neural network tasks are performed to demonstrate the capabilities of our device. First, image preprocessing is performed for handwritten digits and fashion product classification as a general task. Next, resistive-type glove sensor signals are reversed and applied to the photodetector as an input for gesture recognition. Finally, spectroscopic data processing for binary gas mixture classification is demonstrated by utilizing the broadband performance of the device from 3.65 to 3.8 μm. By extending the wavelength from near-infrared to mid-infrared, our work shows the capability of a waveguide-integrated tunable graphene photodetector as a viable weighting solution for photonic in-sensor computing. Furthermore, such a solution could be used for large-scale neuromorphic in-sensor computing in photonic integrated circuits at the edge.