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

Andrea Zazzi; Arka Dipta Das; Lukas Hüssen; Renato Negra; Jeremy Witzens

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arXiv · 2305.06040

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

Andrea Zazzi·Arka Dipta Das·Lukas Hüssen·Renato Negra·Jeremy Witzens

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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 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.

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Andrea Zazzi, Arka Dipta Das, Lukas Hüssen, Renato Negra, Jeremy Witzens. 2023-10-19. Scalable orthogonal delay-division multiplexed OEO artificial neural network trained for TI-ADC equalization. https://arxiv.org/abs/2305.06040

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