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

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 on220 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 m W optical comb and a signal-to-noise ratio of 21.3 d B 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.