Complex-valued trainable activation function hardware using a TCO/silicon modulator

Abstract

Artificial neural network-based electro-optic chipsets constitute a very promising platform because of its remarkable energy efficiency, dense wavelength parallelization possibilities and ultrafast modulation speeds, which can accelerate computation by many orders of magnitude. Furthermore, since the optical field carries information in both amplitude and phase, photonic hardware can be leveraged to naturally implement complex-valued neural networks (CVNNs). Operating with complex numbers may double the internal degrees of freedom as compared with real-valued neural networks, resulting in twice the size of the hardware network and, thus, increased performance in the convergence and stability properties. To this end, the present work revolves on the concept of CVNNs by offering a design, and simulation demonstration, for an electro-optical dual phase and amplitude modulator implemented by integrating a transparent conducting oxide (TCO) in a silicon waveguide structure. The design is powered by the enhancement of the optical-field confinement effect occurring at the epsilon-near-zero (ENZ) condition, which can be tuned electro-optically in TCOs. Operating near the ENZ resonance enables large changes on the real and imaginary parts of the TCO’s permittivity. In this way, phase and amplitude (dual) modulation can be achieved in single device. Optimal design rules are discussed in-depth by exploring device’s geometry and voltage-dependent effects of carrier accumulation inside the TCO film. The device is proposed as a complex-valued activation function for photonic neural systems and its performance tested by simulating the training of a photonic hardware neural network loaded with our custom activation function.