CITATION.cff

cff-version: 1.2.0
title: "Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing"
message: 'If you use this software, please cite it as below.'
preferred-citation:
  type: article
  authors:
  - family-names: Pintus
    given-names: Paolo
  - family-names: Dumont
    given-names: Mario
  - family-names: Shah
    given-names: Vivswan
  - family-names: Murai
    given-names: Toshiya
  - family-names: Shoji
    given-names: Yuya
  - family-names: Huang
    given-names: Duanni
  - family-names: Moody
    given-names: Galan
  - family-names: Bowers
    given-names: John E.
  - family-names: Youngblood
    given-names: Nathan
  doi: "10.1038/s41566-024-01549-1"
  journal: "Nature Photonics"
  title: "Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing"
  year: 2024
authors:
  - family-names: Pintus
    given-names: Paolo
  - family-names: Dumont
    given-names: Mario
  - family-names: Shah
    given-names: Vivswan
  - family-names: Murai
    given-names: Toshiya
  - family-names: Shoji
    given-names: Yuya
  - family-names: Huang
    given-names: Duanni
  - family-names: Moody
    given-names: Galan
  - family-names: Bowers
    given-names: John E.
  - family-names: Youngblood
    given-names: Nathan
identifiers:
  - type: doi
    value: 10.1038/s41566-024-01549-1
    description: >-
      The concept DOI for the collection containing
      all versions of the Citation File Format.
repository-code: 'https://github.com/Vivswan/NonReciprocalRingResonators'
url: "https://www.nature.com/articles/s41566-024-01549-1"
abstract: >-
  Processing information in the optical domain promises advantages in both speed
  and energy efficiency over existing digital hardware for a variety of emerging
  applications in artificial intelligence and machine learning. A typical
  approach to photonic processing is to multiply a rapidly changing 
  optical input vector with a matrix of fixed optical weights. However, 
  encoding these weights on-chip using an array of photonic memory cells 
  is currently limited by a wide range of material- and device-level issues, such 
  as the programming speed, extinction ratio and endurance, among others. Here we 
  propose a new approach to encoding optical weights for in-memory photonic computing 
  using magneto-optic memory cells comprising heterogeneously integrated 
  cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. 
  We show that leveraging the non-reciprocal phase shift in such magneto-optic materials 
  offers several key advantages over existing architectures, providing a fast (1 ns), 
  efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for 
  on-chip optical processing.
keywords:
  - photonics
  - analog-computing
  - magneto-optics
  - photonic in-memory computing
  - artificial intelligence
  - optical weights
  - magneto-optic materials
license: GPL-3.0