Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory

Authors

  • Mario Miscuglio Department of Electrical and Computer Engineering George Washington University Washington DC 20052, USA
  • Jiawei Meng Department of Electrical and Computer Engineering George Washington University Washington DC 20052, USA
  • Armin Mehrabian Department of Electrical and Computer Engineering George Washington University Washington DC 20052, USA
  • Volker J. Sorger Department of Electrical and Computer Engineering George Washington University Washington DC 20052, USA
  • Omer Yesiliurt Birck Nanotechnology Center School of ECE, Purdue University West Lafayette, IN 47907, USA,
  • Ludmila J. Prokopeva Birck Nanotechnology Center School of ECE, Purdue University West Lafayette, IN 47907, USA,
  • Alexander V. Kildishev Birck Nanotechnology Center School of ECE, Purdue University West Lafayette, IN 47907, USA,
  • Yifei Zhang Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge, MA, USA
  • Juejun Hu Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge, MA, USA

Keywords:

GSST, phase change memories, photonic memories

Abstract

Here we present a multi-level discrete-state nonvolatile photonic memory based on an ultra-compact (<4μm) hybrid phase change material GSST-silicon Mach Zehnder modulator, with low insertion losses (3dB), to serve as node in a photonic neural network. Emulating an opportunely trained 100 × 100 fully connected multilayered perceptron neural network with this weighting functionality embedded as photonic memory, shows up to 92% inference accuracy and robustness towards noise when performing predictions of unseen data.

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Published

2020-11-07

How to Cite

[1]
Mario Miscuglio, “Artificial Synapse with Mnemonic Functionality using GSST-based Photonic Integrated Memory”, ACES Journal, vol. 35, no. 11, pp. 1447–1448, Nov. 2020.

Issue

2020: Vol 35 Iss 11

Section

General Submission