High Precision Conductance Modulation in CuCrP<sub>2</sub>S<sub>6</sub> Synaptic Devices for Enhanced Neuromorphic Computing

Abstract Artificial synapses are essential components for realizing neuromorphic computing at the physical level. Although numerous artificial synaptic devices have been fabricated in recent years, their performance is often limited by their resistance state modulation capabilities and stability. Developing artificial synaptic devices with a high number of intermediate states, excellent linearity, and ultralow power consumption remains a challenge. This work presents a neuromorphic synaptic device based on a van der Waals layered ionic conductor material, CuCrP 2 S 6 (CCPS). By precisely controlling the ionic conductivity of the device, it exhibits exceptional biomimetic synaptic behaviors, including long‐term potentiation (LTP) and depression (LTD) with up to 8000 intermediate states (13‐bit), an exceptional nonlinearity of &lt;0.31, and operating energy consumption of &lt;45 pJ per pulse. Importantly, the LTP and LTD behaviors demonstrate outstanding stability, sustaining reliable modulation over 32 cycles. A convolutional neural network (CNN) based on the device's synaptic performance achieves recognition accuracy approaching full precision simulation in image recognition tasks. Additionally, the device shows significant advantages in processing complex auditory signals, achieving a recognition accuracy of 96.4% for sound signals, highlighting its potential in complex sound recognition applications.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-05-05
DOI
https://doi.org/10.1002/adfm.202504017
Citations
6
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
4.38

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article

High Precision Conductance Modulation in CuCrP2S6 Synaptic Devices for Enhanced Neuromorphic Computing

Hai Huang, Junhao Chu, Yu Zhuang, Qianyi Yang et al.
6 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
4.38
article

High Precision Conductance Modulation in CuCrP2S6 Synaptic Devices for Enhanced Neuromorphic Computing

Hai Huang, Junhao Chu, Yu Zhuang, Qianyi Yang, Yanan Cao, Jianlu Wang, Wu Shi, Zhipeng Zhong, Wan Wang, Xiang Li, Xiangjian Meng, Xin Cheng
article en
6 citations

Abstract

Abstract Artificial synapses are essential components for realizing neuromorphic computing at the physical level. Although numerous artificial synaptic devices have been fabricated in recent years, their performance is often limited by their resistance state modulation capabilities and stability. Developing artificial synaptic devices with a high number of intermediate states, excellent linearity, and ultralow power consumption remains a challenge. This work presents a neuromorphic synaptic device based on a van der Waals layered ionic conductor material, CuCrP 2 S 6 (CCPS). By precisely controlling the ionic conductivity of the device, it exhibits exceptional biomimetic synaptic behaviors, including long‐term potentiation (LTP) and depression (LTD) with up to 8000 intermediate states (13‐bit), an exceptional nonlinearity of &lt;0.31, and operating energy consumption of &lt;45 pJ per pulse. Importantly, the LTP and LTD behaviors demonstrate outstanding stability, sustaining reliable modulation over 32 cycles. A convolutional neural network (CNN) based on the device's synaptic performance achieves recognition accuracy approaching full precision simulation in image recognition tasks. Additionally, the device shows significant advantages in processing complex auditory signals, achieving a recognition accuracy of 96.4% for sound signals, highlighting its potential in complex sound recognition applications.

Advanced Functional MaterialsVol. 35(42)
Anhui University of Science and Technology (CN), Fudan University (CN), Shanghai Institute of Technical Physics (CN)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 6%
Advanced Memory and Neural Computing
4.38
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