An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Lai, Xi-Cai; Tang, Zhenhua; Fang, Junlin; Feng, Leyan; Yao, Di-Jie; Zhang, Li; Jiang, Yan-Ping; Liu, Qiu-Xiang; Tang, Xin-Gui; Zhou, Yi-Chun; Shang, Jie; Zhong, Gao-Kuo; Gao, Ju

doi:10.1039/d4mh00064a

Mater. Horiz.

2024

DOI: 10.1039/d4mh00064a

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An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Xi-Cai Lai,

Zhenhua Tang,

Junlin Fang

et al.

Abstract: Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of the traditional von Neumann computing architecture. In this work, an adjustable...

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Cited by 2 publications

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Multimodal In‐Sensor Computing Implemented by Easily‐Fabricated Oxide‐Heterojunction Optoelectronic Synapses

Fang,

Ma,

Wang

et al. 2024

Adv Funct Materials

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The advent of big data and the Internet of Things has created urgent demands for in‐sensor computing hardware with multimodal perception that can effectively resolve the inefficiency, high latency, and excessive energy consumption challenges faced by conventional sensory systems. Here, a simple‐structured optoelectronic synaptic device of In2O3·SnO2/Nb:SrTiO3 (ITO/NSTO) heterostructure is proposed, which vividly demonstrates in‐sensor computing and multimodal perception capabilities. First, with the ingenious synaptic responses of the device under both optical and electrical stimuli, a multimodal in‐sensor neuromorphic computing system capable of concurrently perceiving and processing visual and auditory information is constructed. Using this multimodal system to perform a human emotion recognition task, the misjudgment arising from single‐modal cognition can thereby be effectively avoided. Second, utilizing the integrated sensing and processing functions, along with the dynamic memories of the device array, a neuromorphic vision system is implemented for real‐time monitoring of moving vehicles, which displays high recognition efficiency and accuracy. This research not only provides an optoelectronic synaptic device that is low‐cost and easy to mass‐produce, but also paves the way for next‐generation in‐sensor computing that can efficiently process multimodal signals.

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Multimodal In‐Sensor Computing Implemented by Easily‐Fabricated Oxide‐Heterojunction Optoelectronic Synapses

Fang,

Ma,

Wang

et al. 2024

Adv Funct Materials

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Complex Dynamical Behavior of Locally Active Discrete Memristor-Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees

Liu,

Wang,

Cao

et al. 2024

Electronics

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In continuous neural modeling, memristor coupling has been investigated widely. Yet, there is little research on discrete neural networks in the field. Discrete models with synaptic crosstalk are even less common. In this paper, two locally active discrete memristors are used to couple two discrete Aihara neurons to form a map called DMCAN. Then, the synapse is modeled using a discrete memristor and the DMCAN map with crosstalk is constructed. The DMCAN map is investigated using phase diagram, chaotic sequence, Lyapunov exponent spectrum (LEs) and bifurcation diagrams (BD). Its rich and complex dynamical behavior, which includes attractor coexistence, state transfer, Feigenbaum trees, and complexity, is systematically analyzed. In addition, the DMCAN map is implemented in hardware on a DSP platform. Numerical simulations are further validated for correctness. Numerical and experimental findings show that the synaptic connections of neurons can be modeled by discrete memristor coupling which leads to the construction of more complicated discrete neural networks.

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An adjustable multistage resistance switching behavior of a photoelectric artificial synaptic device with a ferroelectric diode effect for neuromorphic computing

Abstract: Neuromorphic computing, which mimics biological neural networks, is widely regarded as the optimal solution for addressing the limitations of the traditional von Neumann computing architecture. In this work, an adjustable...

Cited by 2 publications

References 55 publications

Multimodal In‐Sensor Computing Implemented by Easily‐Fabricated Oxide‐Heterojunction Optoelectronic Synapses

Multimodal In‐Sensor Computing Implemented by Easily‐Fabricated Oxide‐Heterojunction Optoelectronic Synapses

Complex Dynamical Behavior of Locally Active Discrete Memristor-Coupled Neural Networks with Synaptic Crosstalk: Attractor Coexistence and Reentrant Feigenbaum Trees

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