Spintronic memristor synapse and its RWC learning algorithm

Chen, Jing; Duan, Shukai; Dong, Zhekang; Wang, Lidan

doi:10.1049/iet-cds.2017.0427

Cited by 5 publications

(5 citation statements)

References 29 publications

(46 reference statements)

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“…Chen et al designed a synaptic circuit composed of three memristors and two transistors [26], as shown in Figure 4. The input voltage is V in , and the output V out is the voltage difference between A and B.…”

Section: Other Digital Synaptic Structuresmentioning

confidence: 99%

Research Progress of Neural Synapses Based on Memristors

Chen

et al. 2023

Electronics

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The memristor, characterized by its nano-size, nonvolatility, and continuously adjustable resistance, is a promising candidate for constructing brain-inspired computing. It operates based on ion migration, enabling it to store and retrieve electrical charges. This paper reviews current research on synapses using digital and analog memristors. Synapses based on digital memristors have been utilized to construct positive, zero, and negative weights for artificial neural networks, while synapses based on analog memristors have demonstrated their ability to simulate the essential functions of neural synapses, such as short-term memory (STM), long-term memory (LTM), spike-timing-dependent plasticity (STDP), spike-rate-dependent plasticity (SRDP), and paired-pulse facilitation (PPF). Furthermore, synapses based on analog memristors have shown potential for performing advanced functions such as experiential learning, associative learning, and nonassociative learning. Finally, we highlight some challenges of building large-scale artificial neural networks using memristors.

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Section: Other Digital Synaptic Structuresmentioning

confidence: 99%

Research Progress of Neural Synapses Based on Memristors

Chen

et al. 2023

Electronics

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“…In addition, the memristive function of DW motion in a MTJ by experiments was verified. , By resolving the Landau-Lifshitz-Gilbert equation with STT, Huang et al put forward a spintronic memristor based on the magnetic Skyrmion number to represent analog information, as shown in Figure c, which is another concept of the spintronic memristors Figure d–g shows the simulation diagram of the spintronic memristor Figure d presents the relationship between the input square wave voltage and time, and the I – V curve was obtained under applied voltage (Figure e).…”

Section: Introductionmentioning

confidence: 99%

“…58 Figure 3d−g shows the simulation diagram of the spintronic memristor. 59 Figure 3d presents the relationship between the input square wave voltage and time, and the I−V curve was obtained under applied voltage (Figure 3e). By analyzing the change of memristor, it can be concluded that when the voltage was applied to the spintronic memristor and reached a certain level, the resistance state will be changed.…”

Section: Introductionmentioning

confidence: 99%

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From Spintronic Memristors to Quantum Computing

Qin

Sun

Zhou

et al. 2023

ACS Materials Lett.

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The high-speed development of the Internet of Things and artificial intelligence is revolutionizing the world in terms of industrial production, environmental protection, medical treatment, education, daily life, and so on. The powerful and fast computing methods are crucial for the advanced computing technology toward the next generation artificial intelligence. Traditional computing systems have separated logical and storage units, which cause computation time delays and increase power consumption. Spintronic memristors combine the nonvolatile characteristics of memristors with the scalability of a spin-transfer torque device, which can meet the high-speed, low-power, and scalability requirements of quantum computing (QC) for quantitative information processing. This paper reviews the research progress of spintronic memristors based on magnetic tunnel junction (MTJ), domain wall (DW) motion, and spin wave (SW), respectively, focusing on the development and challenges of spintronic memristors for QC. Finally, some problems that need to be solved urgently in the current research are summarized, and the potential applications of spintronic memristors are discussed.

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“…To date, various memristor synaptic circuit methods have been designed, such as learning algorithms and associative learning algorithms. [8,9] In this case, it is of great significance to establish a good memristor application research model.…”

Section: Introductionmentioning

confidence: 99%

An Improved Memristor Model Based on the Electrochemical Metallization Effect as a Synapse for Biomimetic Applications

Dai

Wang

Yang

2021

Physica Status Solidi (b)

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Herein, an improved memristor model with a controllable forgetting rate due to ion diffusion is proposed. A synaptic learning circuit based on this model is simulated with PSpice to complete synaptic bionics. First, the pulse pair superposition method, which is consistent with biological phenomena, is used to verify spike timing‐dependent plasticity (STDP); this proves that the modified model simulates biological synaptic behavior. Second, a habituation circuit constructed from a memristor and an analog behavioral modeling (ABM) device is designed to realize nonassociative learning. Finally, a new circuit with the improved model is built for Pavlov's associative memory implementation, simplifying the complexity of the circuit. The improved model can better realize the basic learning and memory modes of organisms and thus provide more brain‐inspired computing options.

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Spintronic memristor synapse and its RWC learning algorithm

Cited by 5 publications

References 29 publications

Research Progress of Neural Synapses Based on Memristors

Research Progress of Neural Synapses Based on Memristors

From Spintronic Memristors to Quantum Computing

An Improved Memristor Model Based on the Electrochemical Metallization Effect as a Synapse for Biomimetic Applications

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