Roadmap on Material-Function Mapping for Photonic-Electronic Hybrid Neural Networks

TL;DR

This paper presents a detailed roadmap for developing hybrid photonic-electronic neural networks, focusing on material choices and integration strategies to overcome current limitations in optical memory and nonlinear functionalities.

Contribution

It introduces a comprehensive analysis of materials like ITO for implementing perceptron weights and nonlinear activation functions in hybrid photonic-electronic neural networks.

Findings

ITO can enable both weights and activation functions in photonic neural nodes.

A set of materials exists that can address electronic contacts, memory, and modulation constraints.

Identifies challenges for integrating memory materials into photonic platforms for real-time neural processing.

Abstract

Driven by machine-learning tasks neural networks have demonstrated useful capabilities as nonlinear hypothesis classifiers. The underlying technologies performing the dot product multiplication, the summation, and the nonlinear thresholding on the input data in electronics, however, are limited by the same capacitive challenges known from electronic integrated circuits. The optical domain, in contrast, provides low delay interconnectivity suitable for such node distributed non Von Neumann architectures relying on dense node to node communication. Thus, once the neural network's weights are set, the delay of the network is just given by the time of flight of the photon, which is in the picosecond range for photonic integrated circuits. However, the functionality of memory for storing the trained weights does not exists in optics, thus demanding a fresh look to explore synergies between photonics and electronics in neural networks. 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's perceptron, discussing how it can be realized in hybrid photonic electronic heterogeneous technologies. We show that a set of materials exist that exploit synergies with respect to a number of constrains including electronic contacts, memory functionality, electrooptic modulation, optical nonlinearity, and device packaging. We find that the material ITO, in particular, could provide a viable path for both the perceptron weights and the nonlinear activation function, while simultaneously being a foundry process near material. We finally identify a number of challenges that, if solved, could accelerate the adoption of such heterogeneous integration strategies of emerging memory materials into integrated photonics platforms for real time responsive neural networks.