On-chip phase change optical matrix multiplication core

  • Xuan Li
  • , Nathan Youngblood
  • , Wen Zhou
  • , Johannes Feldmann
  • , Jacob Swett
  • , Samarth Aggarwal
  • , A. Sebastian
  • , C. David Wright
  • , Wolfram Pernice
  • , Harish Bhaskaran

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

18 Scopus citations

Abstract

We demonstrated a non-volatile photonic matrix computation core which contains a 3×3 photonic phase change in-memory computing matrix to carry out matrix vector multiplication on a silicon-based substrate. We established an optical computing core model and hardware implementation based on our photonic phase change material (PCM) devices and fabricated a 3×3 matrix using the silicon photonic foundry. We demonstrated the functionality of this matrix as the linear convolution layer in a convolutional neural network (CNN) and demonstrated simple pattern recognition. Finally, we simulated scaling up matrix limits using experimental data from smaller matrices. This on-chip, non-volatile, photonic computation matrix transfers optical computing from single device to matrix, paving the way for practical wide usage of optical computing systems.

Original languageEnglish
Title of host publication2020 IEEE International Electron Devices Meeting, IEDM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7.5.1-7.5.4
ISBN (Electronic)9781728188881
DOIs
StatePublished - 12 Dec 2020
Externally publishedYes
Event66th Annual IEEE International Electron Devices Meeting, IEDM 2020 - Virtual, San Francisco, United States
Duration: 12 Dec 202018 Dec 2020

Publication series

NameTechnical Digest - International Electron Devices Meeting, IEDM
Volume2020-December
ISSN (Print)0163-1918

Conference

Conference66th Annual IEEE International Electron Devices Meeting, IEDM 2020
Country/TerritoryUnited States
CityVirtual, San Francisco
Period12/12/2018/12/20

Fingerprint

Dive into the research topics of 'On-chip phase change optical matrix multiplication core'. Together they form a unique fingerprint.

Cite this