  The increasing demand for efficient hardware in neural computation highlights
the limitations of electronic-based systems in terms of speed, energy
efficiency, and scalability. Wavelength-division multiplexing (WDM) photonic
neural networks offer a high-bandwidth, low-latency alternative but require
effective photonic activation functions. Here, we propose a power-efficient and
tunable-bias all-optical nonlinear activation function using vanadium dioxide
(VO2) for WDM photonic neural networks. We engineered a SiN/BTO waveguide with
a VO2 patch to exploit the phase-change material's reversible
insulator-to-metal transition (IMT) for nonlinear activation. We conducted
numerical simulations to optimize the waveguide geometry and VO2 parameters,
minimizing propagation and coupling losses while achieving a strong nonlinear
response and low-threshold activation power. Our proposed device features a
sub-milliwatt threshold power, a footprint of 5 $\mu$m, and an ELU-like
activation function. Temporal dynamics show a rise time as low as 5 $\mu$s.
Moreover, the bias of our device could be thermally tuned, improving the speed
and power efficiency. On the other hand, performance evaluations using the
CIFAR-10 dataset confirmed the device's potential for convolutional neural
networks (CNN). Our results show that a hybrid VO2/SiN/BTO platform could play
a prominent role in the path towards the development of high-performance
photonic neural networks.
