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Related Experiment Video

Updated: Feb 17, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

Published on: March 9, 2019

Analog-Tunable Nonvolatile Regulator-Free Memristors for Neuromorphic Controlling.

Seung Jun Ki¹, Shiwoo Lee¹, Xiaoqiu An¹

¹Mechanical Engineering Department, University of Michigan, Ann Arbor, Michigan 48109, United States.

|February 16, 2026

Summary

This summary is machine-generated.

New bismuth selenide (Bi2Se3) memristors offer stable analog tuning and nonvolatile retention for neuromorphic computing. These devices enable ultralow power consumption in hardware reservoir computing networks.

Keywords:

Bi2Se3 in-memory computing memristors nanofabrication reservoir computing

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Last Updated: Feb 17, 2026

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Published on: March 9, 2019

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Published on: May 13, 2020

Area of Science:

Materials Science
Nanotechnology
Computer Engineering

Background:

Analog-tunable memristors lack retention for neuromorphic applications.
Conventional nonvolatile memristors exhibit abrupt switching, requiring current regulators.

Purpose of the Study:

To develop vertically arranged Bi2Se3 memristors for scalable neuromorphic computing.
To overcome limitations of current memristor technologies for synaptic applications.

Main Methods:

Fabrication of Bi2Se3 memristors using gold-assisted physical vapor deposition (PVD).
Characterization of analog conductance tuning, retention, and switching voltages.
Implementation in an all-hardware reservoir computing network.

Main Results:

Bi2Se3 memristors show 10-40% analog tuning with stable retention (<1% loss over 10^4 s).
Low switching voltages (-0.98 V SET, +0.7 V RESET) achieved via electromigration.
Reservoir computing network demonstrated ultralow power consumption (∼7 μW) with competitive performance (NRMSE = 0.094).

Conclusions:

Bi2Se3 memristors provide a robust solution for nonvolatile synaptic nodes.
These devices enable energy-efficient, hardware-based neuromorphic computing.
The developed memristors are suitable for scalable production and advanced computing architectures.