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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