Noise analysis of photonic modulator neurons

Thomas Ferreira de Lima, Alexander N. Tait, Hooman Saeidi, Mitchell A. Nahmias, Hsuan Tung Peng, Siamak Abbaslou, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implementing analog photonic neurons and scalable networks. Here, we examine modulator-based photonic neuron circuits with passive and active transimpedance gains, with special attention to the sources of noise propagation. We find that a modulator nonlinear transfer function can suppress noise, which is necessary to avoid noise propagation in hardware neural networks. In addition, while efficient modulators can reduce power for an individual neuron, signal-to-noise ratios must be traded off with power consumption at a system level. Active transimpedance amplifiers may help relax this tradeoff for conventional p-n junction silicon photonic modulators, but a passive transimpedance circuit is sufficient when very efficient modulators (i.e., low C and low V-pi) are employed.

Original languageEnglish (US)
Article number8782580
JournalIEEE Journal of Selected Topics in Quantum Electronics
Volume26
Issue number1
DOIs
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Keywords

  • Analog links
  • Neural networks
  • Neuromorphic computing
  • Neuromorphic photonics

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