Advanced Multifunctional Memristor Based on Kondo Insulator Gd‐Doped SmB <sub>6</sub> Nanobelt for Artificial Synapses and Neurons
Abstract Memristor is believed as an ideal solution for high‐density storage and neuromorphic computing, which can simultaneouly integrate volatile with nonvolatile functionalities. Seeking excellent resistive‐switching materials and probing their underlying physical mechanism remain great challenges until now. Among most candidates, Kondo insulator SmB 6 nanobelt attracts much interest because its capaciouslattice space and abundant valency electrons are very beneficial for the formation of Ag conductive filaments (CFs). In this work, multifunctional memristor are successfully fabricated based on Gd‐doped SmB 6 nanobelt, interchanging among threshold (TS), self‐rectifying (SR) and bipolar resistance switching (BRS) states by regulating the compliance current. The memristor exhibits a low set/reset voltage (0.92 V/−0.28 V), ultrahigh ON/OFF ratio (≈10 5 ), fast switching time (30 ns/35 ns) and extra‐low power consumption (≈7 fJ), overwhelming most known memristors. Furthermore, the memristor can operate well at high temperature up to 300 °C, and still maintain outstanding resistive‐switching characteristics with a slight decay less than 5% even after a month’s continuous operation. Their transition mechanism from volatile to non‐volatile state can be attributed to the surface self‐diffusion effect of Ag CFs. Consequently, this research may shed new light on promoting advanced neuromorphic computing based on nanomaterials.
Authors
- Jun Chen (ORCID: https://orcid.org/0000-0001-9693-6628)
- Zairan Liu
- Shaozhi Deng (ORCID: https://orcid.org/0000-0003-1830-2026)
- Kai You Wang (ORCID: https://orcid.org/0000-0002-6170-4744)
- Junfeng Xie
- Haojian Lin (ORCID: https://orcid.org/0000-0002-4076-5943)
- Gang Cao
- Yan Tian
- Liu Fei (ORCID: https://orcid.org/0000-0001-7603-9436)
Institutions
- Sun Yat-sen University (CN)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-05-30
- DOI
- https://doi.org/10.1002/adfm.202505133
- Citations
- 1
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.73
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China