Design automation of photonic resonator weights

Thomas Ferreira De Lima, Eli A. Doris, Simon Bilodeau, Weipeng Zhang, Aashu Jha, Hsuan Tung Peng, Eric C. Blow, Chaoran Huang, Alexander N. Tait, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

Neuromorphic photonic processors based on resonator weight banks are an emerging candidate technology for enabling modern artificial intelligence (AI) in high speed analog systems. These purpose-built analog devices implement vector multiplications with the physics of resonator devices, offering efficiency, latency, and throughput advantages over equivalent electronic circuits. Along with these advantages, however, often come the difficult challenges of compensation for fabrication variations and environmental disturbances. In this paper, we review sources of variation and disturbances from our experiments, as well as mathematically define quantities that model them. Then, we introduce how the physics of resonators can be exploited to weight and sum multiwavelength signals. Finally, we outline automated design and control methodologies necessary to create practical, manufacturable, and high accuracy/precision resonator weight banks that can withstand operating conditions in the field. This represents a road map for unlocking the potential of resonator weight banks in practical deployment scenarios.

Original languageEnglish (US)
JournalNanophotonics
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

Keywords

  • RF photonics
  • programmable photonics
  • silicon photonics

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