Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

Abstract Neuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (> 10 7 ), large storage windows (equivalent to ≈95 V), excellent charge retention capability (> 10 4 s), good endurance (> 10 3 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-09-17
DOI
https://doi.org/10.1002/adfm.202408435
Citations
29
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
6.05

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article

Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

Jinran Yu, Qijun Sun, Mingxia Chen, Liuqi Cheng et al.
29 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
6.05
article

Brain‐inspired Multimodal Synaptic Memory via Mechano‐photonic Plasticized Asymmetric Ferroelectric Heterostructure

Jinran Yu, Qijun Sun, Mingxia Chen, Liuqi Cheng, Jie Gong, Zhenyu Feng, Zhong Lin Wang, Lin Li, Yichen Wei, Yifei Wang
article en
29 citations

Abstract

Abstract Neuromorphic devices capable of emulating biological synaptic behaviors are crucial for implementing brain‐like information processing and computing. Emerging 2D ferroelectric neuromorphic devices provide an effective means of updating synaptic weight aside from conventional electrical/optical modulations. Here, by further synergizing with an energy‐efficient synaptic plasticity strategy, a multimodal mechano‐photonic synaptic memory device based on 2D asymmetric ferroelectric heterostructure is presented, which can be modulated by external mechanical behavior and light illumination. By integrating the asymmetric ferroelectric heterostructured field‐effect transistor and a triboelectric nanogenerator, the mechanical displacement‐derived triboelectric potential is ready for gating, programming, and plasticizing the synaptic device, resulting in superior electrical properties of high on/off ratios (> 10 7 ), large storage windows (equivalent to ≈95 V), excellent charge retention capability (> 10 4 s), good endurance (> 10 3 cycles), and primary synaptic behaviors. Besides, optical illumination can effectively synergize with mechanoplasticity to implement multimodal spatiotemporally correlated dynamic logic. The demonstrated multimodal memory synapse provides a facile and promising strategy for multifunctional sensory memory, interactive neuromorphic devices, and future brain‐like electronics embodying artificial intelligence.

Advanced Functional MaterialsVol. 34(48)
Georgia Institute of Technology (US), Guangxi University (CN), Chinese Academy of Sciences (CN), Beijing Institute of Nanoenergy and Nanosystems (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Central South University, State Key Laboratory of High Performance Complex Manufacturing, Beijing Nova Program, National Key Research and Development Program of China, Fundamental Research Funds for the Central Universities, Shanghai Institute of Technical Physics, Chinese Academy of Sciences
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
6.05
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