Improving the Inference Accuracy of Diffractive Optical Neural Networks using Class-Specific Differential Detection

  • Jingxi Li
  • , Deniz Mengu
  • , Yi Luo
  • , Yair Rivenson
  • , Aydogan Ozcan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We report all-optical object classification systems that are based on class-specific design of diffractive neural networks followed by a differential detection scheme. The blind inference accuracies achieved through this framework are significantly enhanced.

Original languageEnglish
Title of host publication2020 Conference on Lasers and Electro-Optics, CLEO 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781943580767
StatePublished - 1 May 2020
Externally publishedYes
Event2020 Conference on Lasers and Electro-Optics, CLEO 2020 - San Jose, United States
Duration: 10 May 202015 May 2020

Publication series

NameConference Proceedings - Lasers and Electro-Optics Society Annual Meeting-LEOS
Volume2020-May
ISSN (Print)1092-8081

Conference

Conference2020 Conference on Lasers and Electro-Optics, CLEO 2020
Country/TerritoryUnited States
CitySan Jose
Period10/05/2015/05/20

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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