Emulation of multiple-functional synapses using V2C memristors with coexistence of resistive and threshold switching

Wang, Yu; Shen, Daqi; Liang, Yilei; Zhao, Yize; Chen, Xintong; Zhou, Lei; Xu, Jianguang; Liu, Xiaoyan; Hu, Ertao; Wang, Lei; Xu, Rongqing; Tong, Yi

doi:10.1016/j.mssp.2021.106123

Cited by 16 publications

(14 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, when the applied voltage was not strong enough, filaments were spontaneously ruptured resulting from the Joule heat effect and minimum energy positions [45,46]. Furthermore, our previous work has presented the transition from volatile to non-volatile switching realized by increasing ICC in the SET process [37]. In conclusion, this unique non-nonlinearity of the TS mechanism can be applied to act as a potential for selectors of large-scale RRAM arrays and a biological emulator of neural behaviors [46,47].…”

Section: Resultsmentioning

confidence: 99%

“…For example, previous reports have been investigated on the MXene (Ti 3 C 2 )-based memristors to realize fast pulse modulation time and emulation of neuromorphic behaviors [20,[33][34][35][36]. In particular, it is reported that three-atom-type MXene (e.g., V 2 C) exhibited ultra-low power, more stable endurance, and multiple synaptic functions, i.e., short-term plasticity (STP), long-term plasticity (LTP), spike-timing-dependent plasticity (STDP), and spike-rate-dependent plasticity (SRDP), due to its more stable atomic structure and higher conductivity [37][38][39]. Notably, to date, there are few reports on emulating an artificial neuron by three-atom-type MXene-based memristors.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

Self Cite

View full text Add to dashboard Cite

Artificial synapses and neurons are two critical, fundamental bricks for constructing hardware neural networks. Owing to its high-density integration, outstanding nonlinearity, and modulated plasticity, memristors have attracted emerging attention on emulating biological synapses and neurons. However, fabricating a low-power and robust memristor-based artificial neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a single two-dimensional (2D) MXene(V2C)-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, originating from the Ag diffusion-based filamentary mechanism. Moreover, our V2C-based artificial neurons faithfully achieve multiple neural functions including leaky integration, threshold-driven fire, self-relaxation, and linear strength-modulated spike frequency characteristics. This work demonstrates that three-atom-type MXene (e.g., V2C) memristors may provide an efficient method to construct the hardware neuromorphic computing systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…[310] A Cu/MXene/Cu memristor device displayed volatile to non-volatile RS transformation, which can be explained by the space-charge-limited current (SCLC) model associated with the electrochemical metallization of the Cu electrode, has been reported by Tong and co-workers. [311] The conductive mechanism was also discussed with Schottky emission [312] and diffusive Ag cations [313] in Ag/V 2 C/Pt devices by the same group. Modulation of the surface functional groups of MXenes by controlling the oxidization can turn the insulating properties and ion migration of the MXenes.…”

Section: Ordered Memristor Arraysmentioning

confidence: 99%

Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications

Zhang

Qin

Zhang

et al. 2023

Adv Funct Materials

View full text Add to dashboard Cite

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain‐inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high‐dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor‐implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor‐implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on‐demand application scenarios. Furthermore, recent advances in the application of memristor‐based physical RC systems are surveyed. In the end, the further prospects of memristor‐implemented RC system in a material view are envisaged.

show abstract

“…In addition, threshold switching memristors can also be used to emulate the functions of biological synapses [22][23][24][25]. Sun et al reported Co-Ni layered, double hydroxide-based memristors, which were used to emulate synaptic functions in biology, including short-term plasticity (STP), long-term plasticity (LTP), paired-pulse facilitation (PPF), and spiking-timing-dependent plasticity (STDP) [25].…”

Section: Introductionmentioning

confidence: 99%

Artificial visual neuron based on threshold switching memristors

Wen

Zhu²,

Guo³

2023

Neuromorph. Comput. Eng.

View full text Add to dashboard Cite

The human visual system encodes optical information perceived by photoreceptors in the retina into neural spikes and then processes them by the visual cortex, with high efficiency and low energy consumption. Inspired by this information processing mode, an universal artificial neuron constructed with a resistor (Rs) and a threshold switching memristor can realize rate coding by modulating pulse parameters and the resistance of Rs. Owing to the absence of an external parallel capacitor, the artificial neuron has minimized chip area. In addition, an artificial visual neuron is proposed by replacing Rs in the artificial neuron with a photo-resistor. The oscillation frequency of the artificial visual neuron depends on the distance between the photo-resistor and light, which is fundamental to acquiring depth perception for precise recognition and learning. A visual perception system with the artificial visual neuron can accurately and conceptually emulate the self-regulation process of the speed control system in driverless automobiles. Therefore, the artificial visual neuron can process efficiently sensory data, reduce or eliminate data transfer and conversion at sensor/processor interfaces, and expand its application in the field of artificial intelligence.

show abstract

Emulation of multiple-functional synapses using V2C memristors with coexistence of resistive and threshold switching

Cited by 16 publications

References 45 publications

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Artificial Neurons Based on Ag/V2C/W Threshold Switching Memristors

Functional Materials for Memristor‐Based Reservoir Computing: Dynamics and Applications

Artificial visual neuron based on threshold switching memristors

Contact Info

Product

Resources

About