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Neural networks are one of the first major milestones in developingartificial intelligence systems. The utilisation of integrated photonics inneural networks offers a promising alternative approach to microelectronic andhybrid optical-electronic implementations due to improvements in computationalspeed and low energy consumption in machine-learning tasks. However, atpresent, most of the neural network hardware systems are still electronic-baseddue to a lack of optical realisation of the nonlinear activation function.Here, we experimentally demonstrate two novel approaches for implementing anall-optical neural nonlinear activation function based on utilising uniquelight-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 configurationsmay serve as nonlinear units in photonic neural networks, while their nonlineartransfer function can be flexibly designed to optimise the performance ofdifferent neuromorphic tasks, depending on the operating wavelength. Theproposed configurations are reconfigurable and can therefore be adjusted forvarious applications without the need to modify the physical structure. Weconfirm the capability and feasibility of the obtained results inmachine-learning applications via an Modified National Institute of Standardsand Technology (MNIST) handwritten digit classifications task, with near 99%accuracy. Our developed concept for an all-optical neuron is expected toconstitute a major step towards the realization of all-optically implementeddeep neural networks.

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