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.
Authors
- Graeme J. Stasiuk (ORCID: https://orcid.org/0000-0002-0076-2246)
- M. Grazia Francesconi (ORCID: https://orcid.org/0000-0002-0124-2197)
- Neil Timothy Kemp (ORCID: https://orcid.org/0000-0002-1906-4608)
- Ayoub H. Jaafar (ORCID: https://orcid.org/0000-0001-7305-4542)
- Thomas Braben
- Craig Venables (ORCID: https://orcid.org/0009-0006-6404-4346)
- Salim Khalfan Suroor Al Habsi
Institutions
- University of Nottingham (GB)
- King's College London (GB)
- University of Hull (GB)
- King's College School (GB)
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
Funders
- 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