Roadmap on material-function mapping for photonic-electronic hybrid neural networks

Abstract

The state-of-the-art hardware in artificial neural networks is still affected by the same capacitive challenges known from electronic integrated circuits. Unlike other emerging electronic technologies, photonics provides low-delay interconnectivity suitable for node-distributed non-von Neumann architectures, relying on dense node-to-node communication. Here, we provide a roadmap to pave the way for emerging hybridized photonic-electronic neural networks by taking a detailed look into a single node perceptron. We discuss how it can be realized in hybrid photonic-electronic heterogeneous technologies. Furthermore, we assess that electro-optic devices based on phase change or strong carrier dispersive effects could provide a viable path for both the perceptron “weights” and the nonlinear activation function in trained neural networks, while simultaneously being foundry process-near materials. This study also assesses the advantages of using nonlinear optical materials as efficient and instantaneous activation functions. We finally identify several challenges that, if solved, could accelerate the adoption of such heterogeneous integration strategies of emerging memory materials into integrated photonics platforms for near real-time responsive neural networks.