Scalable orthogonal delay-division multiplexed OEO artificial neural network trained for TI-ADC equalization

Abstract

We propose a new signaling scheme for on-chip optical-electrical-optical artificial neural networks that utilizes orthogonal delay-division multiplexing and pilot-tone-based self-homodyne detection. This scheme offers a more efficient scaling of the optical power budget with increasing network complexity. Our simulations, based on 220 nm silicon-on-insulator silicon photonics technology, suggest that the network can support 31×31 neurons, with 961 links and freely programmable weights, using a single 500 mW optical comb and a signal-to-noise ratio of 21.3 dB per neuron. Moreover, it features a low sensitivity to temperature fluctuations, ensuring that it can be operated outside of a laboratory environment. We demonstrate the network’s effectiveness in nonlinear equalization tasks by training it to equalize a time-interleaved analog-to-digital converter (ADC) architecture, achieving an effective number of bits over 4 over the entire 75 GHz ADC bandwidth. We anticipate that this network architecture will enable broadband and low latency nonlinear signal processing in practical settings such as ultra-broadband data converters and real-time control systems.