Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications
The combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, including photonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonic integrated circuits,...
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doaj-5bdc8b296e3d45d49a625fc3d36472a52020-11-25T02:34:31ZengAIP Publishing LLCAPL Materials2166-532X2019-09-0179091113091113-710.1063/1.5111840017909APMBehavioral modeling of integrated phase-change photonic devices for neuromorphic computing applicationsSantiago G.-C. Carrillo0Emanuele Gemo1Xuan Li2Nathan Youngblood3Andrew Katumba4Peter Bienstman5Wolfram Pernice6Harish Bhaskaran7C. David Wright8Department of Engineering, University of Exeter, Exeter, United KingdomDepartment of Engineering, University of Exeter, Exeter, United KingdomDepartment of Materials, University of Oxford, Oxford, United KingdomDepartment of Materials, University of Oxford, Oxford, United KingdomGhent University - imec, Ghent, BelgiumGhent University - imec, Ghent, BelgiumInstitute of Physics, University of Münster, Münster, GermanyDepartment of Materials, University of Oxford, Oxford, United KingdomDepartment of Engineering, University of Exeter, Exeter, United KingdomThe combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, including photonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonic integrated circuits, so potentially delivering large-scale all-optical arithmetic-logic units and neuromorphic processing chips. To facilitate in the design and optimization of such large-scale systems, and to aid in the understanding of device and system performance, fast yet accurate computer models are needed. Here, we describe the development of a behavioral modeling tool that meets such requirements, being capable of essentially instantaneous modeling of the write, erase, and readout performance of various integrated phase-change photonic devices, including those for synaptic and neuronal mimics.http://dx.doi.org/10.1063/1.5111840 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Santiago G.-C. Carrillo Emanuele Gemo Xuan Li Nathan Youngblood Andrew Katumba Peter Bienstman Wolfram Pernice Harish Bhaskaran C. David Wright |
spellingShingle |
Santiago G.-C. Carrillo Emanuele Gemo Xuan Li Nathan Youngblood Andrew Katumba Peter Bienstman Wolfram Pernice Harish Bhaskaran C. David Wright Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications APL Materials |
author_facet |
Santiago G.-C. Carrillo Emanuele Gemo Xuan Li Nathan Youngblood Andrew Katumba Peter Bienstman Wolfram Pernice Harish Bhaskaran C. David Wright |
author_sort |
Santiago G.-C. Carrillo |
title |
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
title_short |
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
title_full |
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
title_fullStr |
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
title_full_unstemmed |
Behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
title_sort |
behavioral modeling of integrated phase-change photonic devices for neuromorphic computing applications |
publisher |
AIP Publishing LLC |
series |
APL Materials |
issn |
2166-532X |
publishDate |
2019-09-01 |
description |
The combination of phase-change materials and integrated photonics has led to the development of new forms of all-optical devices, including photonic memories, arithmetic and logic processors, and synaptic and neuronal mimics. Such devices can be readily fabricated into photonic integrated circuits, so potentially delivering large-scale all-optical arithmetic-logic units and neuromorphic processing chips. To facilitate in the design and optimization of such large-scale systems, and to aid in the understanding of device and system performance, fast yet accurate computer models are needed. Here, we describe the development of a behavioral modeling tool that meets such requirements, being capable of essentially instantaneous modeling of the write, erase, and readout performance of various integrated phase-change photonic devices, including those for synaptic and neuronal mimics. |
url |
http://dx.doi.org/10.1063/1.5111840 |
work_keys_str_mv |
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