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Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics

Leisheng Jin, Yiming Cao, Zhuo Liu, Tao Liu, Lijie Li Orcid Logo

Physical Review E, Volume: 110, Issue: 4, Start page: 045307

Swansea University Author: Lijie Li Orcid Logo

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DOI (Published version): 10.1103/PhysRevE.110.045307

Abstract

In this work, we aim to unveil the general correlations between the performance of a physical reservoir computing (RC) system and the inherent nonlinear dynamics of the adopted device. Taking the metal-ferroelectric-metal (MFM) capacitor, one of the most popular candidate devices for compute-in-memo...

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Published in: Physical Review E
ISSN: 2470-0045 2470-0053
Published: American Physical Society (APS) 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa68443
first_indexed 2024-12-03T19:48:14Z
last_indexed 2024-12-03T19:48:14Z
id cronfa68443
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spelling 2024-12-03T12:08:57.5737769 v2 68443 2024-12-03 Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics ed2c658b77679a28e4c1dcf95af06bd6 0000-0003-4630-7692 Lijie Li Lijie Li true false 2024-12-03 ACEM In this work, we aim to unveil the general correlations between the performance of a physical reservoir computing (RC) system and the inherent nonlinear dynamics of the adopted device. Taking the metal-ferroelectric-metal (MFM) capacitor, one of the most popular candidate devices for compute-in-memory (CIM) technology, as the computational platform, we construct a nonlinear dynamical model of polarization in the ferroelectric layer. We then design the physical RC utilizing a single and/or an array of MFM capacitors by analyzing the model's stability and feasible dynamical cases. Subsequently, both the initial task and benchmark are numerically conducted to verify the designed RC's superiority. It is proven that by selecting an appropriate dynamical case, the RC can achieve a recognition rate as high as 96.13%, surpassing the results reported in previous work. Finally, we discuss how these key parameters play their role in the RC's performance from the perspective of affecting the system's transient responses, nonlinearity, and short-fading memory. This work paves the foundation for designing highly efficient reservoir computing based on MFM capacitors as well as other memristive devices such as memristors, tunneling diodes, etc. Journal Article Physical Review E 110 4 045307 American Physical Society (APS) 2470-0045 2470-0053 17 10 2024 2024-10-17 10.1103/PhysRevE.110.045307 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Not Required The work was supported by the Research incubation fund (No. NY223091) from Nanjing University of Posts and Telecommunications. 2024-12-03T12:08:57.5737769 2024-12-03T11:59:50.8528705 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering Leisheng Jin 1 Yiming Cao 2 Zhuo Liu 3 Tao Liu 4 Lijie Li 0000-0003-4630-7692 5
title Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
spellingShingle Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
Lijie Li
title_short Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
title_full Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
title_fullStr Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
title_full_unstemmed Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
title_sort Highly efficient ferroelectric capacitor reservoir computing through the study of its nonlinear polarization dynamics
author_id_str_mv ed2c658b77679a28e4c1dcf95af06bd6
author_id_fullname_str_mv ed2c658b77679a28e4c1dcf95af06bd6_***_Lijie Li
author Lijie Li
author2 Leisheng Jin
Yiming Cao
Zhuo Liu
Tao Liu
Lijie Li
format Journal article
container_title Physical Review E
container_volume 110
container_issue 4
container_start_page 045307
publishDate 2024
institution Swansea University
issn 2470-0045
2470-0053
doi_str_mv 10.1103/PhysRevE.110.045307
publisher American Physical Society (APS)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Electronic and Electrical Engineering
document_store_str 0
active_str 0
description In this work, we aim to unveil the general correlations between the performance of a physical reservoir computing (RC) system and the inherent nonlinear dynamics of the adopted device. Taking the metal-ferroelectric-metal (MFM) capacitor, one of the most popular candidate devices for compute-in-memory (CIM) technology, as the computational platform, we construct a nonlinear dynamical model of polarization in the ferroelectric layer. We then design the physical RC utilizing a single and/or an array of MFM capacitors by analyzing the model's stability and feasible dynamical cases. Subsequently, both the initial task and benchmark are numerically conducted to verify the designed RC's superiority. It is proven that by selecting an appropriate dynamical case, the RC can achieve a recognition rate as high as 96.13%, surpassing the results reported in previous work. Finally, we discuss how these key parameters play their role in the RC's performance from the perspective of affecting the system's transient responses, nonlinearity, and short-fading memory. This work paves the foundation for designing highly efficient reservoir computing based on MFM capacitors as well as other memristive devices such as memristors, tunneling diodes, etc.
published_date 2024-10-17T08:36:51Z
_version_ 1821393930909908992
score 11.04748

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