Phase-change nanoclusters embedded in a memristor for simulating synaptic learning

Wan, Qin; Zeng, Fei; Yin, Jungang; Sun, Yiming; Hu, Yuandong; Liu, Jialu; Wang, Yingcong; Li, Guoqi; Guo, Dong; Pan, Feng

doi:10.1039/c8nr09765h

Cited by 25 publications

(25 citation statements)

References 40 publications

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“…Artificial synapses have been developed based on various structures, materials, and mechanisms to mimic the structure and synaptic plasticity of biological synapses. Conventional memory mechanisms (e.g., conductive filament, [54,75,[77][78][79][80][81][82][83][84][85][86] Schottky junction, [87][88][89] charge trapping, [82,[90][91][92][93][94][95] phase change, [75,[96][97][98] ferroelectricity, [99][100][101][102][103][104][105][106][107] ion migration [4,51,74,108] ) have been extended to the implementation of synaptic properties. Artificial synapses, i.e., transistors that exploit electrochemical reactions have also been developed.…”

Section: Mechanismsmentioning

confidence: 99%

“…Phase Change: Phase-change memory exploits a reversible phase change of materials from amorphous to crystalline by Joule heating. [75,[96][97][98] The amorphous state is a HRS, and the crystalline state is a LRS. When a current is applied, Joule heating causes the temperature to rise to crystallization temperature of the material; this is referred to as the "set" operation.…”

Section: Two-terminal Devicesmentioning

confidence: 99%

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Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

et al. 2019

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Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next‐generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high‐density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.

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Section: Mechanismsmentioning

confidence: 99%

Section: Two-terminal Devicesmentioning

confidence: 99%

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

et al. 2019

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“…Various synaptic devices based on resistive switching, driven by different physical working mechanisms such as active metallic filament, charge trapping/detrapping effect, ions/vacancies migration, phase change behaviors, ferroelectric polarization, and spin‐transfer torque‐based synapses, have been demonstrated for emerging memory and neuromorphic computing. Many scientists are actively working to resolve various issues in those synaptic devices: high energy consumption, low switching speed, poor reliability, or the lack of high device density for integration.…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Synaptic Devices Based on 2D Materials

Sun

Wang

Yang

2020

Advanced Intelligent Systems

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Diverse synaptic plasticity with a wide range of timescales in biological synapses plays an important role in memory, learning, and various signal processing with exceptionally low power consumption. Emulating biological synaptic functions by electric devices for neuromorphic computation has been considered as a way to overcome the traditional von Neumann architecture in which separated memory and information processing units require high power consumption for their functions. Synaptic devices are expected to conduct complex signal processing such as image classification, decision‐making, and pattern recognition in artificial neural networks. Among various materials and device architectures for synaptic devices, 2D materials and their van der Waals (vdW) heterostructures have been attracting tremendous attention from researchers based on their capacity to mimic unique synaptic plasticity for neuromorphic computing. Herein, the basic operations of biological synapses and physical properties of 2D materials are discussed, and then 2D materials and their vdW heterostructures for advanced synaptic operations with novel working mechanisms are reviewed. In particular, there is a focus on how to design synaptic devices with the vdW structures in terms of critical 2D materials and their limitations, providing insight into the emerging synaptic device systems and artificial neural networks with 2D materials.

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“…[9,10] Formerly, artificial synapses were realized by complementary metal-oxide-semiconductor (CMOS) circuitry containing dozens of electronic components. [12][13][14][15][16] Also, for an ideal synapse device it is better to meet these requirements, such as symmetric potentiation-depression characteristics, 5-bit/cell analog levels, and high non-volatility with ≈100 conductance ON/OFF ratio. As comparison, two-terminal memristors, especially resistive random access memory and phase change random access memory, that recently entered our field of vision have been widely discussed as artificial synapses owing to their structures which is similar to that of synapses and the reproducible tuning of resistance.…”

mentioning

confidence: 99%

Designing High‐Performance Storage in HfO₂/BiFeO₃ Memristor for Artificial Synapse Applications

Liu

Xiong

Liu

et al. 2020

Adv Elect Materials

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using artificial synapses is an essential step to accomplish the neuromorphic computing system. [9,10] Formerly, artificial synapses were realized by complementary metal-oxide-semiconductor (CMOS) circuitry containing dozens of electronic components. [11] However, many electronic components result in complicated architecture and high energy consumption. As comparison, two-terminal memristors, especially resistive random access memory and phase change random access memory, that recently entered our field of vision have been widely discussed as artificial synapses owing to their structures which is similar to that of synapses and the reproducible tuning of resistance. [12][13][14][15][16] Also, for an ideal synapse device it is better to meet these requirements, such as symmetric potentiation-depression characteristics, 5-bit/cell analog levels, and high non-volatility with ≈100 conductance ON/OFF ratio. They are the key points we need to take into account. [17,18] In particular, HfO 2 -based memristors have been demonstrated as a leading alternative as synapse in virtue of its distinctive superiority, such as simple structure, <10 ns switching speed, <10 pJ power consumption, multilevel ability, and compatibility with CMOS fabrication process. [19][20][21] However, the resistance contrast (ON/OFF ratio) of HfO 2 -based memristors ranges from ≈40 to ≈150. [22] Considering the resistance fluctuation of these memristors across a silicon wafer, larger ON/OFF ratio is needed to guarantee high recognition accuracy (>97%). [23] Recently, several works were reported on improvement of the memristors' performance with thin interfacial layer. [24][25][26] As one of the most important multiferroic materials, bismuth iron oxide with perovskite structure has come into notice for its potential in multifunctional device applications. [27] Moreover, BiFeO 3 (BFO) has attracted much attention because it possesses superior characteristics of resistance switching (RS) such as large ON/OFF ratio in some researches. [28] It is also confirmed that BFO can be applied in bi-layer design memristor to significantly improve RS characteristics, [29] which gives inspiration to insert BFO thin film as the goal of high device performances.In this work, ultrathin BFO film was inserted to fabricate Pt/BFO/HfO 2 /TiN memristor to improve the RS characteristic of HfO 2 -based memristor. The material characterization and RS behavior were systemically analyzed. The role of the inserting BFO layer on the RS behavior was evaluated and the RS mechanism triggered by inserting BFO film was explored.HfO 2 -based memristors that remembers the history of the current that has passed through them have attracted great interest for use as artificial synapses in neuromorphic systems. However, the low resistance contrast exhibited by HfO 2 -based memristors seriously decreases their recognition accuracy. By inserting a 2 nm BiFeO 3 layer a large memory window of 10 4 and remarkable pulse endurance of 10 8 cycles are achieved. Multilevel storage capability is also d...

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Phase-change nanoclusters embedded in a memristor for simulating synaptic learning

Abstract: A type of memristor with structure of Pd/Nb : AlNO/Pd is designed to mimic synaptical plasticity and kinetics.

Cited by 25 publications

References 40 publications

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Recent Progress in Synaptic Devices Based on 2D Materials

Designing High‐Performance Storage in HfO₂/BiFeO₃ Memristor for Artificial Synapse Applications

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Phase-change nanoclusters embedded in a memristor for simulating synaptic learning

Abstract: A type of memristor with structure of Pd/Nb : AlNO/Pd is designed to mimic synaptical plasticity and kinetics.

Cited by 25 publications

References 40 publications

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Recent Progress in Synaptic Devices Based on 2D Materials

Designing High‐Performance Storage in HfO2/BiFeO3 Memristor for Artificial Synapse Applications

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Designing High‐Performance Storage in HfO₂/BiFeO₃ Memristor for Artificial Synapse Applications