A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors

In this study, VO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Mott memristors have been successfully fabricated, leading to the proposal of a passive and scalable high-order neural circuit. This circuit consists of two coupled VO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Mott memristors, two resistors, and three capacitors. The proposed high-order neural circuit demonstrates 11 distinct firing behaviors similar to those of biological neurons, along with controllable burst firing patterns. The spikes, inter-spike interval (ISI) within a burst, and the quiescence interval between bursts can be adjusted by varying the capacitance and resistance values. Additionally, this circuit operates without the need for a bias supply or inductors, enhancing its scalability. This design not only improves circuit interconnection but also effectively reduces power consumption, providing a solid foundation for the development of spiking neural networks (SNNs).

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Publication Details

Journal
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Published
2025-01-01
DOI
https://doi.org/10.1109/jxcdc.2025.3573709
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

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article

A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors

Weihua Liu, Chuanyu Han, Li Geng, Xin Li et al.
1 citations
IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Advanced Memory and Neural Computing
0.73
article

A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors

Weihua Liu, Chuanyu Han, Li Geng, Xin Li, Xiaohui Wu, Shujing Zhao, Zhijie Lin
article en
1 citations

Abstract

In this study, VO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Mott memristors have been successfully fabricated, leading to the proposal of a passive and scalable high-order neural circuit. This circuit consists of two coupled VO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Mott memristors, two resistors, and three capacitors. The proposed high-order neural circuit demonstrates 11 distinct firing behaviors similar to those of biological neurons, along with controllable burst firing patterns. The spikes, inter-spike interval (ISI) within a burst, and the quiescence interval between bursts can be adjusted by varying the capacitance and resistance values. Additionally, this circuit operates without the need for a bias supply or inductors, enhancing its scalability. This design not only improves circuit interconnection but also effectively reduces power consumption, providing a solid foundation for the development of spiking neural networks (SNNs).

IEEE Journal on Exploratory Solid-State Computational Devices and CircuitsVol. 11
Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 28%
Advanced Memory and Neural Computing
0.73
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A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors — Weihua Liu, Chuanyu Han, et al. · IEEE Journal on Exploratory Solid-State Computational Devices and Circuits (2025) | TGRS Research Map | TGRS