Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling
Current analog-tunable memristors often suffer from poor retention, limiting their role as nonvolatile synaptic nodes in hardware-based neuromorphic networks. On the other hand, conventional nonvolatile memristors display abrupt switching, necessitating additional current-regulating devices for circuit function. To address these challenges, we developed vertically arranged Bi<sub>2</sub>Se<sub>3</sub> memristors suitable for crossbar array configuration via gold-assisted, plasma-etching-free physical vapor deposition (PVD), facilitating scalable production. These memristors exhibit notable analog conductance tuning (10-40%) with minimal postsetting relaxation and stable nonvolatile retention (<1% loss over 10<sup>4</sup> s) without the need for a current regulator (e.g., a current-limiting transistor). They operate at low switching voltages (-0.98 V SET, +0.7 V RESET). The superior switching attributes stem from the electromigration-driven filament formation in the Au/Bi<sub>2</sub>Se<sub>3</sub>/Ti structure. Utilizing these memristors, we built an all-hardware-based reservoir computing network where Bi<sub>2</sub>Se<sub>3</sub> memristors serve effectively in the readout layer, processing sensory signals and generating control commands with minimal digital electronics. This configuration achieves ultralow power consumption (∼7 μW), significantly lower than digital equivalents, while maintaining competitive performance, evidenced by a normalized root-mean-square error (NRMSE) of 0.094. These results position Bi<sub>2</sub>Se<sub>3</sub> memristors as robust, energy-efficient options for next-generation neuromorphic computing.
Authors
- J Kim (ORCID: https://orcid.org/0000-0003-2777-8413)
- Shiwoo Lee
- Mingze Chen (ORCID: https://orcid.org/0000-0002-4265-1544)
- Xi Liang (ORCID: https://orcid.org/0000-0002-0674-2246)
- Xiaoqiu An
- Seung Jun Ki
Institutions
- University of Michigan (US)
Publication Details
- Journal
- ACS Nano
- Published
- 2026-02-16
- DOI
- https://doi.org/10.1021/acsnano.5c16447
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00