A low-power Si:HfO2 ferroelectric tunnel memristor for spiking neural networks

Yan, Xiaobing; Jia, Xi; Zhang, Yinxing; Shi, Shu; Wang, Lulu; Shao, Yiduo; Sun, Yong; Sun, Shiqing; Zhao, Zhen; Zhao, Jianhui; Sun, Jiameng; Guo, Zhenqiang; Guan, Zhiyuan; Zhang, Zixuan; Han, Xu; Chen, Jingsheng

doi:10.1016/j.nanoen.2022.108091

Cited by 38 publications

(18 citation statements)

References 54 publications

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“…Based on the superior overall performance of the devices fabricated at 750 °C, subsequent electrical measurements were conducted on devices fabricated at this temperature. The ability of memristor devices to retain their conductance state is crucial for reliability, particularly for demonstrating inference functions . When seven consecutive scans of ±4 V are applied to the device, it can be observed that the current gradually increases/decreases with the +4 V/–4 V sweep cycles, as shown in Figure S4.…”

Section: Resultsmentioning

confidence: 99%

“…The ability of memristor devices to retain their conductance state is crucial for reliability, particularly for demonstrating inference functions. 38 When seven consecutive scans of ±4 V are applied to the device, it can be observed that the current gradually increases/ decreases with the +4 V/−4 V sweep cycles, as shown in Figure S4. The STO:MgO-based device exhibits obvious nonvolatility at the end of each voltage sweep, as shown in Figure 1h.…”

Section: Rs Behaviors and Structural Characterizationsmentioning

confidence: 97%

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High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor

Guo,

Liu,

Sun

et al. 2023

ACS Nano

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Neuromorphic computing based on memristors capable of in-memory computing is promising to break the energy and efficiency bottleneck of well-known von Neumann architectures. However, unstable and nonlinear conductance updates compromise the recognition accuracy and block the integration of neural network hardware. To this end, we present a highly stable memristor with self-assembled vertically aligned nanocomposite (VAN) SrTiO3:MgO films that achieve excellent resistive switching with low set/reset voltage variability (4.7%/–5.6%) and highly linear conductivity variation (nonlinearity = 0.34) by spatially limiting the conductive channels at the vertical interfaces. Various synaptic behaviors are simulated by continuously modulating the conductance. Especially, convolutional image processing using diverse crossbar kernels is demonstrated, and the artificial neural network achieves an overwhelming recognition accuracy of up to 97.50% for handwritten digits. Even under the perturbation of Poisson noise (λ = 10), 6% Salt and Pepper noise, and 5% Gaussian noise, the high recognition accuracies are retained at 95.43%, 94.56%, and 95.97%, respectively. Importantly, the logic memory function is proven experimentally based on the nonvolatile properties. This work provides a material system and design idea to achieve high-performance neuromorphic computing and logic operation.

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Section: Resultsmentioning

confidence: 99%

Section: Rs Behaviors and Structural Characterizationsmentioning

confidence: 97%

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor

Guo,

Liu,

Sun

et al. 2023

ACS Nano

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“…Breakthroughs in powerful hardware and algorithms would bring a revolution of brain-like chips. , For brain-like chips, some automatic design toold, simulator platforms, and integration technology should be developed to support the system-level design of memristor-based computing chips. − The hardware–software codesign flow and platforms from device to algorithm is a foundation to design efficient computing chips. For the nanodevices and nanotechnology, the memristor-based nanodevice primitives should be optimized to meet the AI application requirements of computing chips, and developing integration technology is beneficial to the design of future large-scale computing chips.…”

Section: Discussionmentioning

confidence: 99%

Memristor-Based Artificial Chips

Sun,

Chen,

Zhou

et al. 2023

ACS Nano

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Memristors, promising nanoelectronic devices with in-memory resistive switching behavior that is assembled with a physically integrated core processing unit (CPU) and memory unit and even possesses highly possible multistate electrical behavior, could avoid the von Neumann bottleneck of traditional computing devices and show a highly efficient ability of parallel computation and high information storage. These advantages position them as potential candidates for future data-centric computing requirements and add remarkable vigor to the research of next-generation artificial intelligence (AI) systems, particularly those that involve brain-like intelligence applications. This work provides an overview of the evolution of memristor-based devices, from their initial use in creating artificial synapses and neural networks to their application in developing advanced AI systems and brain-like chips. It offers a broad perspective of the key device primitives enabling their special applications from the view of materials, nanostructure, and mechanism models. We highlight these demonstrations of memristor-based nanoelectronic devices that have potential for use in the field of brain-like AI, point out the existing challenges of memristor-based nanodevices toward brain-like chips, and propose the guiding principle and promising outlook for future device promotion and system optimization in the biomedical AI field.

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“…Compared to works like ref. [141], which have demonstrated synapses capable of performing multiple functions, it may be ideal to investigate multipurpose graphene-based memristive synapses.…”

Section: Graphene Memristive Neuromorphic Networkmentioning

confidence: 99%

A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits

Walters

Jacob

Amirsoleimani

et al. 2023

Advanced Intelligent Systems

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As data processing volume increases, the limitations of traditional computers and the need for more efficient computing methods become evident. Neuromorphic computing mimics the brain's low‐power and high‐speed computations, making it crucial in the era of big data and artificial intelligence. One significant development in this field is the memristor, a device that exhibits neuromorphic tendencies. The performance of memristive devices and circuits relies on the materials used, with graphene being a promising candidate due to its unique properties. Researchers are investigating graphene‐based memristors for large‐scale, sustainable fabrication. Herein, progress in the development of graphene‐based memristive neuromorphic devices and circuits is highlighted. Graphene and its common fabrication methods are discussed. The fabrication and production of graphene‐based memristive devices are reviewed and comparisons are provided among graphene‐ and nongraphene‐based memristive devices. Next, a detailed synthesis of the devices utilizing graphene‐based memristors is provided to implement the basic building blocks of neuromorphic architectures, that is, synapses, and neurons. This is followed by reviewing studies building graphene memristive spiking neural networks (SNNs). Finally, insights on the prospects of graphene‐based neuromorphic memristive systems including their device‐ and network‐level challenges and opportunities are given.

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A low-power Si:HfO2 ferroelectric tunnel memristor for spiking neural networks

Cited by 38 publications

References 54 publications

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor

Memristor-Based Artificial Chips

A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits

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A low-power Si:HfO2 ferroelectric tunnel memristor for spiking neural networks

Cited by 38 publications

References 54 publications

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO3:MgO Film Memristor

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO3:MgO Film Memristor

Memristor-Based Artificial Chips

A Review of Graphene‐Based Memristive Neuromorphic Devices and Circuits

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Product

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High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor

High-Performance Neuromorphic Computing and Logic Operation Based on a Self-Assembled Vertically Aligned Nanocomposite SrTiO₃:MgO Film Memristor