Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors

Wu, Guodong; Feng, Ping; Wan, Xiang; Zhu, Liqiang; Shi, Yi; Wan, Qing

doi:10.1038/srep23578

Cited by 113 publications

(113 citation statements)

References 46 publications

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“…The optical synaptic characteristics of IGZO were measured without inducing polarization in ferroelectric layer. First, in response to optical stimulus, IGZO semiconductor demonstrated PPF which is an STP characteristic in a brain and is essential to processing of temporal information in auditory or visual signals 52,53. In a biological synapse, synaptic strength is temporally increased by successive stimuli when the interval between stimuli is short.…”

Section: Figurementioning

confidence: 99%

Synergistic Improvement of Long‐Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia‐Based Oxide‐Semiconductor Transistors

2020

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A number of synapse devices have been intensively studied for the neuromorphic system which is the next‐generation energy‐efficient computing method. Among these various types of synapse devices, photonic synapse devices recently attracted significant attention. In particular, the photonic synapse devices using persistent photoconductivity (PPC) phenomena in oxide semiconductors are receiving much attention due to the similarity between relaxation characteristics of PPC phenomena and Ca2+ dynamics of biological synapses. However, these devices have limitations in its controllability of the relaxation characteristics of PPC behaviors. To utilize the oxide semiconductor as photonic synapse devices, relaxation behavior needs to be accurately controlled. In this study, a photonic synapse device with controlled relaxation characteristics by using an oxide semiconductor and a ferroelectric layer is demonstrated. This device exploits the PPC characteristics to demonstrate synaptic functions including short‐term plasticity, paired‐pulse facilitation (PPF), and long‐term plasticity (LTP). The relaxation properties are controlled by the polarization of the ferroelectric layer, and this polarization is used to control the amount by which the conductance levels increase during PPF operation and to enhance LTP characteristics. This study provides an important step toward the development of photonic synapses with tunable synaptic functions.

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Section: Figurementioning

confidence: 99%

Synergistic Improvement of Long‐Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia‐Based Oxide‐Semiconductor Transistors

2020

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“…Therefore, they are environment‐friendly and do not require intricate synthesis and large amount of energy . Various novel biopolymers have been researched for memristors and artificial synapses such as lignin, chitosan, and proteins . In 2017, Park and Lee reported an article about flexible lignin‐based synaptic devices.…”

Section: Organic Materialsmentioning

confidence: 99%

“…There are various possible materials that can achieve memristive properties. These include binary oxides, oxide perovskites, polymers, bioinspired materials, 2D materials, halide perovskites, and low‐dimensional materials as shown in Figure . Each material has advantages in the working mechanism and/or properties of itself, which results in improved performances of memristive devices and artificial synapses.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Memristive Materials for Artificial Synapses

Kim

Han

Kim

et al. 2018

Adv Materials Technologies

200

126

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required. Several types of emerging mem ories have been researched in the past few decades such as magnetic memory, phase change memory, ferroelectric tunnel junc tions, and resistive switching memory. Among these emerging devices, resistive switching memory called memristors, introduced by Chua in 1971, [1] have strong points of small cell size, nonvolatile and random data access possibility, easy fabri cation process, and simple structure. [2,3] Because of these advantages, various mate rials are examined for achieving memris tive properties.In addition, different from the past sev eral decades, information is being made depending on experiences or repeated stimuli similar to that in the human brain. The human brain contains ≈10 11 neurons and 10 15 synapses, occupies a small space, and consumes less than 20 W, which is lower than the power required to run a household light bulb. [4][5][6] Moreover, the human brain is currently considered as the most intelligent and fastest operation system. Therefore, neuromorphic computing, which emu lates the human brain, has been regarded as a promising nextgeneration computing system. Studies on neuromorphic computing have been rapidly growing and highlighted for various applications such as artificial intelligence, sensors, robotic devices, and memory devices.Existing neural networks are implemented by the combination of machine learning as software and the von Neumann archi tecture as hardware based on the complementary metaloxide semiconductor (CMOS) technology. However, CMOSbased cir cuits require 6-12 transistors and the design is not flexible. [7] The present computing system with the von Neumann architecture is implemented by a serial operation through a central processing unit (CPU). Because of the von Neumann bottleneck, memory devices have limitations in data processing speed between memory and CPU and require high power and large space. [8][9][10] Therefore, a new neuromorphic computing system that is exe cuted by parallel operation with a high operation speed, low energy consumption, and small volume is critically required.To achieve such requirement, memristive materials have been actively examined as emulating several functions of human brain. A memristor could act as a single unit of synapse without software programming supports. Memristorbased neu romorphic architecture is implemented by parallel operation with efficient power, small volume, and high data processing Neuromorphic architectures are in the spotlight as promising candidates for substituting current computing systems owing to their high operation speed, scale-down ability, and, especially, low energy consumption. Among candidate materials, memristors have shown excellent synaptic behaviors such as spike time-dependent plasticity and spike rate-dependent plasticity by gradually changing their resistance state according to electrical input stimuli. Memristor can work as a single synapse without programming support, which remarkably satisfies the requirements of neuromorphic computing. Here, the mo...

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“…Numerous synaptic plasticity with different time course have been demonstrated in various memristive systems based on oxides, [182,183] organics, [184,185] biomaterials, [186,187] etc. Numerous synaptic plasticity with different time course have been demonstrated in various memristive systems based on oxides, [182,183] organics, [184,185] biomaterials, [186,187] etc.…”

Section: Homosynaptic Plasticitymentioning

confidence: 99%

Memristive Synapses and Neurons for Bioinspired Computing

Yang

Huang

Guo

2019

Adv Elect Materials

188

127

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and constant data movement are needed while working. In the human brain, huge and complex neural networks composed of gigantic amounts of neurons massively interconnected by an even larger number of synapses are in charge of computing and memory. Unlike a digital computer, information storage and processing happen at the same time in the brain. [3] Artificial intelligence (AI) that could rival biological neural networks is being continuously pursued by human being. There are two approaches to implement AI: one is the software simulation of neural networks with the aid of digital computers, another is developing hardware-integrated circuits of neural networks. In fact, AI based on the software simulation is evolving very fast thanks to the advances in the development of algorithm in recent years, and it even outperforms the human brain in specific complex tasks, such as playing the game of Go. [4] However, software-based AI suffers from critical issues of enormous power consumption and massive data throughput. Hardware-based AI systems are more efficient in terms of speed and power consumption; however, complementary metal oxide semiconductor (CMOS) transistors are inherently different from the basic building blocks of biological neural networks, neurons, and synapses, in terms of operation dynamic and behavior. A circuit consisting of at least ten transistors are required to realize the function of one biological synapse, which is impractical for the implementation of large networks, not to mention the human brain (roughly 10 11 neurons interconnected by ≈10 15 synapses). [5,6] Fortunately, the emergence of memristive devices with innate dynamics resembling biological synapses and neurons provides a feasible and simple way to build hardware-based bio-inspired computing systems. [7,8] For instance, only one compact memristive device with a metalinsulator-metal (MIM) sandwich structure is sufficient to reproduce some functions of a biological synapse. Moreover, the vector-matrix multiplication, which is believed to be the most data-intensive work in neural networks, can be naturally performed in a cross-bar array of memristive devices on the physical level based on Ohm and Kirchhoff laws. [9,10] These features endow the memristive devices ideally suited to realize highly efficient bioinspired computing systems in hardware.To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state-of-art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biore...

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Artificial Synaptic Devices Based on Natural Chicken Albumen Coupled Electric-Double-Layer Transistors

Cited by 113 publications

References 46 publications

Synergistic Improvement of Long‐Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia‐Based Oxide‐Semiconductor Transistors

Synergistic Improvement of Long‐Term Plasticity in Photonic Synapses Using Ferroelectric Polarization in Hafnia‐Based Oxide‐Semiconductor Transistors

Recent Advances in Memristive Materials for Artificial Synapses

Memristive Synapses and Neurons for Bioinspired Computing

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