A wide-range operating synaptic device based on organic ferroelectricity with low energy consumption

Tu, Li; Yuan, Sijian; Xu, Jiawei; Yang, Kunlong; Wang, Pengfei; Cui, Xiaolei; Zhang, Xin; Wang, Jiao; Zhan, Yiqiang; Zheng, Lirong

doi:10.1039/c8ra04403a

Cited by 27 publications

(32 citation statements)

References 36 publications

(54 reference statements)

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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%

Section: Two-terminal Devicesmentioning

confidence: 99%

“…[75] Ferroelectricity: Ferroelectric materials have a spontaneously polarized dipole moment due to structural asymmetry, even when no voltage is applied. [99][100][101][102][103] The alignment of the dipole can be controlled by applying an electric field to exploit a polarization-electric field hysteresis loop. In ferroelectric synapses, the synaptic weight is modulated by using sequential alignment of voltage-controlled dipoles to control the ferroelectric tunneling barriers (tunnel resistance).…”

Section: Two-terminal Devicesmentioning

confidence: 99%

“…[99] Organic artificial synapses have been developed by using films composed of poly(vinylidene fluoridetrifluoroethylene) (P(VDF-TrFE)), which is an organic ferroelectric material, and poly(9,9-dioctylfluorene) (PFO), with the film sandwiched between ITO and CuPc/Au electrodes ( Figure 5e). [103] The energy-barrier heights at interfaces of PFO/ITO and PFO/ CuPc were gradually controlled with the dipole alignment by an external electric field. Synaptic potentiation/depression were realized according to the conductance change (synaptic weight) of the device.…”

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%

Section: Two-terminal Devicesmentioning

confidence: 99%

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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“…Two-terminal memristive devices are promising candidates to act as a compact electronic element and have been widely demonstrated in the pursuit of certain synaptic functions [8][9][10] . Specifically, synaptic operations, including long-term potentiation/depression (LTP/LTD), short-term potentiation/depression (STP/STD), spiketiming-dependent plasticity (STDP) learning rules, paired-pulse facilitation (PPF), and low-power consumption have been extensively simulated in these electronic synapses (e-synapses) [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] . However, e-synapses usually suffer from unavoidable temporal (cycle-to-cycle) and spatial (device-to-device) variations due to their intrinsic working mechanism and structure interaction.…”

Section: Introductionmentioning

confidence: 99%

Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing

et al. 2019

NPG Asia Mater

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An electronic synapse (e-synapse) based on memristive switching is a promising electronic element that emulates a biological synapse to realize neuromorphic computing. However, the complex resistive switching process it relies on hampers the reproducibility of its performance. Thus, achievement of a reproducible electronic synapse with a high rate of finished products has become a significant challenge in the development of an artificial intelligent circuit. Here, we demonstrate an ultrathin e-synapse having high yield (>95%), minimal performance variation, and extremely low power consumption based on an Al 2 O 3 /graphene quantum dots/Al 2 O 3 sandwich structure that was fabricated using atomic layer deposition. The e-synapse showed both high device-to-device and cycle-to-cycle reproducibility with high stability, endurance, and switching uniformity, because the essential synaptic behaviors could be observed. This implementation of an e-synapse with an Al 2 O 3 /graphene quantum dots/Al 2 O 3 structure should intensify motivation for engineering e-synapses for neuromorphic computing.

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Organic Synaptic Transistors: The Evolutionary Path from Memory Cells to the Application of Artificial Neural Networks

Shao

Zhao

Liu

2021

Adv Funct Materials

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The progress of neural synaptic devices is experiencing an era of explosive growth. Given that the traditional storage system has yet to overcome the von Neumann bottleneck, it is critical to develop hardware with bioinspired information processing functions and lower power consumption. Transistors based on 2D materials, metal oxides, and organic materials have been adopted to mimic the synapse of a human brain, due to their high plasticity, parallel computing, integrated storage, and system information processing. Among these materials used to build transistors, organic semiconductors are considered to be the most promising candidate for neural synaptic devices and bio‐electronics, owing to their easy processing, mechanical flexibility, low cost, good bio‐compatibility, and ductility. This review focuses on the recent advances in organic synaptic devices with various structures, materials, and working mechanisms. The applications of artificial neural networks that integrate multiple organic synaptic transistors are also concretely discussed. Finally, the challenges that organic synaptic devices currently face are discussed and future developments are forecast.

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A wide-range operating synaptic device based on organic ferroelectricity with low energy consumption

Abstract: The two-terminal synaptic device based on organic ferroelectricity with low energy consumption can provide reliable synaptic function.

Cited by 27 publications

References 36 publications

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics

Ultrathin electronic synapse having high temporal/spatial uniformity and an Al2O3/graphene quantum dots/Al2O3 sandwich structure for neuromorphic computing

Organic Synaptic Transistors: The Evolutionary Path from Memory Cells to the Application of Artificial Neural Networks

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