Transparent conductive oxides as a material platform for photonic neural networks

TL;DR

This paper proposes transparent conductive oxides as a promising material platform for photonic neural networks, enabling ultrafast, low-power neuromorphic photonics with potential to overcome electronic processing limitations.

Contribution

It introduces transparent conductive oxides as a novel material platform capable of supporting nonlinear and bistable behaviors essential for photonic neural network implementation.

Findings

Transparent conductive oxides exhibit nonlinearity and bistability under optical and electrical inputs.

They are suitable for integration into photonic neural network architectures.

This approach could enable ultrafast, low-energy neuromorphic photonic computing.

Abstract

Photonics integrated circuits have a huge potential to serve as a framework for a new class of information processing machines and can enable ultrafast artificial neural networks. They can overcome the existing speed and power limits of the electronic processing elements and provide additional benefits of photonics such as high-bandwidth, sub-nanosecond latencies and low-energy interconnect credentials leading to a new paradigm called neuromorphic photonics. The main obstacle to realize such a task is a lack of proper material platform that imposes serious requirements on the architecture of the network. Here we suggest and justify that transparent conductive oxides can be an excellent candidate for such a task as they provide a nonlinearity and bistability under both optical and electrical inputs.