Threshold-Switching Memristors for Neuromorphic Thermoreception

Neuromorphic devices emulating the temperature-sensing capabilities of biological thermoreceptors hold significant promise for neuron-like artificial sensory systems. In this work, Bi<sub>2</sub>Se<sub>3</sub>-based threshold-switching memristors were presented in constructing temperature-sensing neuron circuits, leveraging its exceptional attributes, such as high switching ratio (>10<sup>6</sup>), low threshold voltage, and thermoelectric response. The spiking oscillation response of the devices to resistance and temperature variations was analyzed using Hspice simulation of the memristor model based on its resistance in on/off states, threshold voltage (<i>V<sub>th</sub></i>), and hold voltage (<i>V<sub>hold</sub></i>). These results show the great potential of the Bi<sub>2</sub>Se<sub>3</sub>-based memristor in enabling biorealistic thermoreception applications.

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Publication Details

Journal
Sensors
Published
2025-03-01
DOI
https://doi.org/10.3390/s25051533
Citations
3
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
2.19

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article

Threshold-Switching Memristors for Neuromorphic Thermoreception

Chunsheng Jiang, Qilin Hua, Haotian Li
3 citations
Sensors
Advanced Memory and Neural Computing
2.19
article

Threshold-Switching Memristors for Neuromorphic Thermoreception

Chunsheng Jiang, Qilin Hua, Haotian Li
article en
3 citations

Abstract

Neuromorphic devices emulating the temperature-sensing capabilities of biological thermoreceptors hold significant promise for neuron-like artificial sensory systems. In this work, Bi<sub>2</sub>Se<sub>3</sub>-based threshold-switching memristors were presented in constructing temperature-sensing neuron circuits, leveraging its exceptional attributes, such as high switching ratio (>10<sup>6</sup>), low threshold voltage, and thermoelectric response. The spiking oscillation response of the devices to resistance and temperature variations was analyzed using Hspice simulation of the memristor model based on its resistance in on/off states, threshold voltage (<i>V<sub>th</sub></i>), and hold voltage (<i>V<sub>hold</sub></i>). These results show the great potential of the Bi<sub>2</sub>Se<sub>3</sub>-based memristor in enabling biorealistic thermoreception applications.

SensorsVol. 25(5)
Beijing Institute of Technology (CN), Guangxi Normal University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 13%
Advanced Memory and Neural Computing
2.19
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Threshold-Switching Memristors for Neuromorphic Thermoreception — Chunsheng Jiang, Qilin Hua, et al. · Sensors (2025) | TGRS Research Map | TGRS