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.

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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
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article

Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling

J Kim, Shiwoo Lee, Mingze Chen, Xi Liang et al.
ACS Nano
Advanced Memory and Neural Computing
article

Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling

J Kim, Shiwoo Lee, Mingze Chen, Xi Liang, Xiaoqiu An, Seung Jun Ki
article en

Abstract

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.

ACS NanoVol. 20(8)
University of Michigan (US)
Affordable and clean energy
Openalex Percentile: Top 54%
Advanced Memory and Neural Computing
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Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling — J Kim, Shiwoo Lee, et al. · ACS Nano (2026) | TGRS Research Map | TGRS