An electroforming-free, analog interface-type memristor based on a SrFeOx epitaxial heterojunction for neuromorphic computing

Rao, Jingjing; Fan, Zhen; Hong, Liang; Cheng, Shuangshuang; Huang, Qicheng; Zhao, Jisen; Xiang, Xuepeng; Guo, Er‐Jia; Guo, Haizhong; Hou, Zhipeng; Chen, Y.; Lu, Xubing; Zhou, Guofu; Gao, Xingsen; Liu, J.-M.

doi:10.1016/j.mtphys.2021.100392

Cited by 51 publications

(50 citation statements)

References 70 publications

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“…In the interface bipolar type, conducting defects are distributed throughout the metal electrode-switching layer interface [20]. Gradual resistive switching is observed in the interface type, and it is beneficial for achieving multilevel states [21,22]. Low-current operation is necessary to realize high-density in a cross-point RRAM array.…”

Section: Introductionmentioning

confidence: 99%

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Park

Lee

et al. 2021

Metals

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In this work, we fabricated a Pt/SiN/TaN memristor device and characterized its resistive switching by controlling the compliance current and switching polarity. The chemical and material properties of SiN and TaN were investigated by X-ray photoelectron spectroscopy. Compared with the case of a high compliance current (5 mA), the resistive switching was more gradual in the set and reset processes when a low compliance current (1 mA) was applied by DC sweep and pulse train. In particular, low-power resistive switching was demonstrated in the first reset process, and was achieved by employing the negative differential resistance effect. Furthermore, conductance quantization was observed in the reset process upon decreasing the DC sweep speed. These results have the potential for multilevel cell (MLC) operation. Additionally, the conduction mechanism of the memristor device was investigated by I-V fitting.

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Section: Introductionmentioning

confidence: 99%

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Park

Lee

et al. 2021

Metals

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“…The synaptic weight of the network is defined as the conductance difference between two equivalent optical synapses: W = G + − G − . [ 49 , 50 , 51 ] And the update of ANN parameters is implemented according to the synaptic plasticity (LTP and LTD), and the recognition accuracy is highly related to the linearity of the weight update trajectory. Figure 5c depicts the LTP and LTD curves in flat, bending, and folding states, in which 200 continuous optical spikes were applied to the device (405 nm, 2 Hz) and followed by another 200 negative voltage pulses (−5 V, 2 Hz).…”

Section: Resultsmentioning

confidence: 99%

Flexible Artificial Optoelectronic Synapse based on Lead‐Free Metal Halide Nanocrystals for Neuromorphic Computing and Color Recognition

et al. 2022

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Optoelectronic synapses combining optical‐sensing and synaptic functions are playing an increasingly vital role in the neuromorphic computing systems development, which can efficiently process visual information and complex recognition, memory, and learning. Metal halides are considered promising candidates for synaptic devices due to their excellent optoelectronic properties. However, the toxicity of lead and the further development of device functions are the recognized problems at present. Herein, a flexible optoelectronic synapses system based on high‐quality lead‐free Cs 3 Bi 2 I 9 nanocrystals is demonstrated, in which the carrier confinement caused by the band mismatching between the Cs 3 Bi 2 I 9 and the organic semiconductor layer provides the possibility to simulate synaptic behaviors. The synaptic functions including long/short‐term memory and learning‐forgetting‐relearning are demonstrated in this device and visual perception, visual memory, and color recognition functions are successfully implemented. Additionally, the flexible device exhibits excellent robustness and can realize imaging of light distribution under curved hemispheres similar to the human eye. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the high‐precision recognition of handwritten digital images and possesses a strong fault tolerant capability even in bending states. These results are expected to drive the practical progress of metal halide for neuromorphic computing.

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“…Here, two datasets are used for evaluation, small images (8 × 8 pixels) of hand-written digits from the “Optical Recognition of Handwritten Digits” (ORHD) dataset [ 63 ] and large images (28 × 28 pixels) of hand-written digits from the “Modified National Institute of Standards and Technology” (MNIST) dataset [ 64 ], and the representative images of the MNIST dataset are illustrated in Figure 6(b) . In the process of neural network simulation based on the WSe 2 QDs device, the weights between the neurons will be mapped to the intersection of the horizontal bar and the vertical bar in the crossbar based on the WSe 2 QDs device (Figure S6 in the Supplementary Material) [ 65 ]. A crossbar, considered part of the “neural core,” is used to perform vector-matrix multiplication and outer product update operations (Figure S7 in the Supplementary Material).…”

Section: Resultsmentioning

confidence: 99%

Superlow Power Consumption Artificial Synapses Based on WSe ₂ Quantum Dots Memristor for Neuromorphic Computing

Wang

Liu

et al. 2022

Research

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As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a ROFF/RON ratio of ~5 × 103, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

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An electroforming-free, analog interface-type memristor based on a SrFeOx epitaxial heterojunction for neuromorphic computing

Cited by 51 publications

References 70 publications

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Flexible Artificial Optoelectronic Synapse based on Lead‐Free Metal Halide Nanocrystals for Neuromorphic Computing and Color Recognition

Superlow Power Consumption Artificial Synapses Based on WSe ₂ Quantum Dots Memristor for Neuromorphic Computing

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An electroforming-free, analog interface-type memristor based on a SrFeOx epitaxial heterojunction for neuromorphic computing

Cited by 51 publications

References 70 publications

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Conductance Quantization Behavior in Pt/SiN/TaN RRAM Device for Multilevel Cell

Flexible Artificial Optoelectronic Synapse based on Lead‐Free Metal Halide Nanocrystals for Neuromorphic Computing and Color Recognition

Superlow Power Consumption Artificial Synapses Based on WSe 2 Quantum Dots Memristor for Neuromorphic Computing

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Superlow Power Consumption Artificial Synapses Based on WSe ₂ Quantum Dots Memristor for Neuromorphic Computing