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Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices

Felder, Daniel ; Femmer, Robert ; Bell, Daniel ; Rall, Deniz ; Pietzonka, Dirk ; Henzler, Sebastian ; Linkhorst, John ; Wessling, Matthias (2022)
Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices.
In: Advanced Theory and Simulations, 5 (6)
doi: 10.1002/adts.202100492
Article, Bibliographie

Abstract

Conductive polymer devices with tunable resistance allow low-energy, linear programming for efficient neuromorphic computing. Depolarizing impurities, however, are difficult to exclude and limit device performance through nonideal writes and self-discharge. It is shown that these phenomena can be numerically described by combining two-phase charge transport models with electrochemical self-discharge. The simulations accurately reproduce the experimental data, including cyclic voltammetry and standard neuromorphic functions, such as linear programming of discrete states and short-term potentiation. Impurities affect device write accuracy significantly for long programming times above 1000 ms. The effect is reduced to 0.03% for shorter times. Self-discharge is impacted by device potential as well as impurity concentration. A model-based trade-off between operating parameters nearly triples the number of usable conductance states at ambient conditions. Understanding these device limitations as well as workarounds is a vital step toward the implementation of neuromorphic device networks.

Item Type: Article
Erschienen: 2022
Creators: Felder, Daniel ; Femmer, Robert ; Bell, Daniel ; Rall, Deniz ; Pietzonka, Dirk ; Henzler, Sebastian ; Linkhorst, John ; Wessling, Matthias
Type of entry: Bibliographie
Title: Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices
Language: English
Date: 2022
Publisher: Wiley
Journal or Publication Title: Advanced Theory and Simulations
Volume of the journal: 5
Issue Number: 6
DOI: 10.1002/adts.202100492
Abstract:

Conductive polymer devices with tunable resistance allow low-energy, linear programming for efficient neuromorphic computing. Depolarizing impurities, however, are difficult to exclude and limit device performance through nonideal writes and self-discharge. It is shown that these phenomena can be numerically described by combining two-phase charge transport models with electrochemical self-discharge. The simulations accurately reproduce the experimental data, including cyclic voltammetry and standard neuromorphic functions, such as linear programming of discrete states and short-term potentiation. Impurities affect device write accuracy significantly for long programming times above 1000 ms. The effect is reduced to 0.03% for shorter times. Self-discharge is impacted by device potential as well as impurity concentration. A model-based trade-off between operating parameters nearly triples the number of usable conductance states at ambient conditions. Understanding these device limitations as well as workarounds is a vital step toward the implementation of neuromorphic device networks.

Uncontrolled Keywords: artificial synapse, conductive polymer, direct numerical simulation, electrochemical random-access memory, memristor, PEDOT:PSS
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Chair for Process Engineering of Electrochemical Systems
Date Deposited: 13 Sep 2023 11:13
Last Modified: 13 Sep 2023 11:13
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