High-speed Ta2O5-based threshold switching memristor for LIF neurons
Due to their high similarity to biological ion channels, low power consumption, small footprint, and the fact that they do not require reset circuits, threshold switching memristors have been intensively studied for simulating neurons in neuromorphic chips. Switching speed is one of the key challenges which limit the application of threshold switching memristors in chips. In this study, Ta2O5 threshold switching memristors with high switching speeds were prepared by doping with silver. The results show that 14 wt. % Ag doped Ta2O5 threshold switching memristors exhibit excellent bi-directional threshold switching performance, featuring fast switching speeds (<20 ns, <18 ns), low leakage currents (<10 pA), and high switching ratio (>107). According to the field nucleation theory, the rapid switching speed can be attributed to the low nucleation energy (0.26 eV) of silver within the Ta2O5 matrix, which is achieved by incorporating 14 wt. % Ag during the doping process. Based on Pspice, a LIF (leaky integrate-and-fire) neuron based on the silver nanoparticles doped Ta2O5 threshold switching memristors is built, and its firing function has been simulated. The results show that the LIF neuron with a short switching time is able to excite pulse spiking with high frequencies. These results demonstrated that the silver nanoparticles doped Ta2O5-based threshold switching memristors hold significant potential for constructing high-speed artificial neural networks.
Authors
- Hongjia Song (ORCID: https://orcid.org/0000-0003-0288-2425)
- Zixi He
- Linyan Yao
- Yiheng Wang (ORCID: https://orcid.org/0000-0002-0263-9313)
- Chuan Ma
- Jinbin Wang (ORCID: https://orcid.org/0000-0001-5192-7648)
- Xiangli Zhong (ORCID: https://orcid.org/0000-0002-2639-9131)
Institutions
- Division of Materials Science and Engineering (AU)
- Xiangtan University (CN)
Publication Details
- Journal
- Journal of Applied Physics
- Published
- 2024-10-11
- DOI
- https://doi.org/10.1063/5.0221088
- Citations
- 5
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.04
Funders
- National Natural Science Foundation of China