Ti$_3$C$_2$T$_x$ MXene Enabled All-Optical Nonlinear Activation Function for On-Chip Photonic Deep Neural Networks

TL;DR

This paper demonstrates novel all-optical nonlinear activation functions using Ti3C2Tx MXene, enabling faster, energy-efficient photonic neural networks with reconfigurable properties and high accuracy in digit classification.

Contribution

The work introduces two innovative MXene-based approaches for all-optical nonlinear activation functions in photonic neural networks, a significant step forward in optical AI hardware.

Findings

Achieved near 99% accuracy on MNIST digit classification.

Demonstrated reconfigurable nonlinear transfer functions.

Validated the feasibility of MXene-based optical nonlinear units.

Abstract

Neural networks are one of the first major milestones in developing artificial intelligence systems. The utilisation of integrated photonics in neural networks offers a promising alternative approach to microelectronic and hybrid optical-electronic implementations due to improvements in computational speed and low energy consumption in machine-learning tasks. However, at present, most of the neural network hardware systems are still electronic-based due to a lack of optical realisation of the nonlinear activation function. Here, we experimentally demonstrate two novel approaches for implementing an all-optical neural nonlinear activation function based on utilising unique light-matter interactions in 2D Ti$_3$C$_2$T$_x$ (MXene) in the infrared (IR) range in two configurations: 1) a saturable absorber made of MXene thin film, and 2) a silicon waveguide with MXene flakes overlayer. These configurations may serve as nonlinear units in photonic neural networks, while their nonlinear transfer function can be flexibly designed to optimise the performance of different neuromorphic tasks, depending on the operating wavelength. The proposed configurations are reconfigurable and can therefore be adjusted for various applications without the need to modify the physical structure. We confirm the capability and feasibility of the obtained results in machine-learning applications via an Modified National Institute of Standards and Technology (MNIST) handwritten digit classifications task, with near 99% accuracy. Our developed concept for an all-optical neuron is expected to constitute a major step towards the realization of all-optically implemented deep neural networks.