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
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Felder, Daniel ; Femmer, Robert ; Bell, Daniel ; Rall, Deniz ; Pietzonka, Dirk ; Henzler, Sebastian ; Linkhorst, John ; Wessling, Matthias |
| Art des Eintrags: | Bibliographie |
| Titel: | Coupled Ionic–Electronic Charge Transport in Organic Neuromorphic Devices |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Verlag: | Wiley |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Advanced Theory and Simulations |
| Jahrgang/Volume einer Zeitschrift: | 5 |
| (Heft-)Nummer: | 6 |
| DOI: | 10.1002/adts.202100492 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | artificial synapse, conductive polymer, direct numerical simulation, electrochemical random-access memory, memristor, PEDOT:PSS |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Verfahrenstechnik elektrochemischer Systeme (VES) |
| Hinterlegungsdatum: | 13 Sep 2023 11:13 |
| Letzte Änderung: | 13 Sep 2023 11:13 |
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