Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles (GeO<sub>2</sub> NP) embedded within a poly(methyl methacrylate) (PMMA) thin film. Besides exhibiting forming-free resistive switching and an uncommon "ON" state in pristine conditions, the hybrid (nanocomposite) devices demonstrate a unique form of mixed-mode switching. The observed stopping voltage-dependent switching enables state-of-the-art bifunctional synaptic behavior with short-term (volatile/temporal) and long-term (nonvolatile/nontemporal) modes that are switchable depending on the stopping voltage applied. The short-term memory mode device is demonstrated to further emulate important synaptic functions such as short-term potentiation (STP), short-term depression (STD), paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-voltage-dependent plasticity (SVDP), spike-duration-dependent plasticity (SDDP), and, more importantly, the "learning-forgetting-rehearsal" behavior. The long-term memory mode gives additional long-term potentiation (LTP) and long-term depression (LTD) characteristics for long-term plasticity applications. The work shows a unique coexistence of the two resistive switching modes, providing greater flexibility in device design for future adaptive and reconfigurable neuromorphic computing systems at the hardware level.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2024-08-12
DOI
https://doi.org/10.1021/acsami.4c07820
Citations
12
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.50

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article

Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties

Graeme J. Stasiuk, M. Grazia Francesconi, Neil Timothy Kemp, Ayoub H. Jaafar et al.
12 citations
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
2.50
article

Unique Coexistence of Two Resistive Switching Modes in a Memristor Device Enables Multifunctional Neuromorphic Computing Properties

Graeme J. Stasiuk, M. Grazia Francesconi, Neil Timothy Kemp, Ayoub H. Jaafar, Thomas Braben, Craig Venables, Salim Khalfan Suroor Al Habsi
article en
12 citations

Abstract

We report on hybrid memristor devices consisting of germanium dioxide nanoparticles (GeO<sub>2</sub> NP) embedded within a poly(methyl methacrylate) (PMMA) thin film. Besides exhibiting forming-free resistive switching and an uncommon "ON" state in pristine conditions, the hybrid (nanocomposite) devices demonstrate a unique form of mixed-mode switching. The observed stopping voltage-dependent switching enables state-of-the-art bifunctional synaptic behavior with short-term (volatile/temporal) and long-term (nonvolatile/nontemporal) modes that are switchable depending on the stopping voltage applied. The short-term memory mode device is demonstrated to further emulate important synaptic functions such as short-term potentiation (STP), short-term depression (STD), paired-pulse facilitation (PPF), post-tetanic potentiation (PTP), spike-voltage-dependent plasticity (SVDP), spike-duration-dependent plasticity (SDDP), and, more importantly, the "learning-forgetting-rehearsal" behavior. The long-term memory mode gives additional long-term potentiation (LTP) and long-term depression (LTD) characteristics for long-term plasticity applications. The work shows a unique coexistence of the two resistive switching modes, providing greater flexibility in device design for future adaptive and reconfigurable neuromorphic computing systems at the hardware level.

ACS Applied Materials & InterfacesVol. 16(33)
University of Nottingham (GB), King's College London (GB), University of Hull (GB), King's College School (GB)
University of Hull, Leverhulme Trust, King's College London, Ministry of Higher Education and Scientific Research, Medical Research Council, Engineering and Physical Sciences Research Council
Affordable and clean energy
Openalex Percentile: Top 10%
Advanced Memory and Neural Computing
2.50
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