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

Photonics Research, vol. 12, pp. 85

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.

Authors 5

1. Andrea Zazzi
2. Arka Dipta Das
3. Lukas Hüssen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Renato Negra Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Jeremy Witzens