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 a 220 nm SOI silicon photonics technology, suggest that the network can support 31 x 31 neurons, with 961 links and freely programmable weights, using a single 500 mW optical comb and an SNR 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 ADC architecture, achieving an ENOB 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.
@article{arxiv.2305.06040,
title = {Scalable orthogonal delay-division multiplexed OEO artificial neural network trained for TI-ADC equalization},
author = {Andrea Zazzi and Arka Dipta Das and Lukas Hüssen and Renato Negra and Jeremy Witzens},
journal= {arXiv preprint arXiv:2305.06040},
year = {2023}
}