Superconducting optoelectronic circuits for neuromorphic computing

We propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes the requirement for time-multiplexing that has limited the event rates of purely electronic platforms. The proposed processing units can operate at $20$ MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm$^2$ for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of $\approx 20$ aJ per synapse event. By leveraging multilayer photonics with low-temperature-deposited waveguides and superconductors with feature sizes $>$ 100 nm, this approach could scale to massive interconnectivity near that of the human brain, and could surpass the brain in speed and energy efficiency.