Silicon microring synapses enable photonic deep learning beyond 9-bit precision

Weipeng Zhang, Chaoran Huang, Hsuan Tung Peng, Simon Bilodeau, Aashu Jha, Eric Blow, Thomas Ferreira de Lima, Bhavin J. Shastri, Paul Prucnal

Research output: Contribution to journalArticlepeer-review

60 Scopus citations


Deep neural networks (DNNs) consist of layers of neurons interconnected by synaptic weights. A high bit-precision in weights is generally required to guarantee high accuracy in many applications. Minimizing error accumulation between layers is also essential when building large-scale networks. Recent demonstrations of photonic neural networks are limited in bit-precision due to cross talk and the high sensitivity of optical components (e.g., resonators). Here, we experimentally demonstrate a record-high precision of 9 bits with a dithering control scheme for photonic synapses. We then numerically simulated the impact with increased synaptic precision on a wireless signal classification application. This work could help realize the potential of photonic neural networks for many practical, real-world tasks.

Original languageEnglish (US)
Pages (from-to)579-584
Number of pages6
Issue number5
StatePublished - May 2022

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics


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