Electropolymerized dopamine-based memristors using threshold switching behaviors for artificial current-activated spiking neurons

Abstract Memristors have a synapse-like two-terminal structure and electrical properties, which are widely used in the construction of artificial synapses. However, compared to inorganic materials, organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance. Here, for the first time, we present an organic memristor based on an electropolymerized dopamine-based memristive layer. This polydopamine-based memristor demonstrates the improvements in key performance, including a low threshold voltage of 0.3 V, a thin thickness of 16 nm, and a high parasitic capacitance of about 1 μ F∙mm −2 . By leveraging these properties in combination with its stable threshold switching behavior, we construct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage, whose spiking frequency increases with the increase of current stimulation analogous to a biological neuron. The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

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Publication Details

Journal
Journal of Semiconductors
Published
2025-02-01
DOI
https://doi.org/10.1088/1674-4926/24070007
Citations
8
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
5.84
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article

Electropolymerized dopamine-based memristors using threshold switching behaviors for artificial current-activated spiking neurons

Yiqiang Zheng, Zhexin Li, Bowen Zhong, Lingchen Liu et al.
8 citations
Journal of Semiconductors
Advanced Memory and Neural Computing
5.84
article

Electropolymerized dopamine-based memristors using threshold switching behaviors for artificial current-activated spiking neurons

Yiqiang Zheng, Zhexin Li, Bowen Zhong, Lingchen Liu, Lili Wang, Zheng Lou, Xiaokun Qin
article en
8 citations

Abstract

Abstract Memristors have a synapse-like two-terminal structure and electrical properties, which are widely used in the construction of artificial synapses. However, compared to inorganic materials, organic materials are rarely used for artificial spiking synapses due to their relatively poor memrisitve performance. Here, for the first time, we present an organic memristor based on an electropolymerized dopamine-based memristive layer. This polydopamine-based memristor demonstrates the improvements in key performance, including a low threshold voltage of 0.3 V, a thin thickness of 16 nm, and a high parasitic capacitance of about 1 μ F∙mm −2 . By leveraging these properties in combination with its stable threshold switching behavior, we construct a capacitor-free and low-power artificial spiking neuron capable of outputting the oscillation voltage, whose spiking frequency increases with the increase of current stimulation analogous to a biological neuron. The experimental results indicate that our artificial spiking neuron holds potential for applications in neuromorphic computing and systems.

Journal of SemiconductorsVol. 46(2)
Chinese Academy of Sciences (CN), Institute of Semiconductors (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 4%
Advanced Memory and Neural Computing
5.84
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