Near ideal synaptic functionalities in Li ion synaptic transistor using Li3POxSex electrolyte with high ionic conductivity

Nikam, Revannath Dnyandeo; Kwak, Myonghoon; Lee, Jongwon; Rajput, Krishn Gopal; Banerjee, Writam; Hwang, Hyunsang

doi:10.1038/s41598-019-55310-8

Cited by 72 publications

(95 citation statements)

References 31 publications

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“…However, electrochemical transistors have suffered from small dynamic range and short retention issues. An electrochemical transistor composed of a Se-doped lithium phosphate (Li 3 PO x Se x ) electrolyte with a high ionic conductivity was proposed as an artificial synapse ( Figure 5 A) ( Nikam et al., 2019 ). For comparison, an electrochemical transistor was also fabricated using lithium phosphate (Li 3 PO 4 ) as an electrolyte that had lower ionic conductivity compared with Li 3 PO x Se x .…”

Section: Artificial Synapses Based On Emerging Materialsmentioning

confidence: 99%

“… (D) Potentiation/depression of Li 3 PO x Se x -based electrochemical transistor using 90 identical pulses (±1.5 V, 1 s). Reproduced with permission ( Nikam et al, 2019 ). Copyright 2019, Springer Nature.…”

Section: Artificial Synapses Based On Emerging Materialsmentioning

confidence: 99%

“…Two-terminal devices such as memristors, ferroelectric tunnel junctions (FTJs), and phase change memories (PCMs) have been researched as artificial synapses ( Jang et al., 2019a ; Li et al., 2020a ; Kuzum et al., 2012a ). Also, three-terminal devices including electrochemical transistors, ferroelectric transistors, and charge-trapping transistors have been researched as artificial synapses ( Nikam et al., 2019 ; Chou et al., 2020 ; Kim et al., 2020a ). Neural networks require emulation of the function of neurons; these functions have been achieved using memristors, PCMs, threshold switching devices, and ferroelectric transistors ( Hua et al., 2019 ; Tuma et al., 2016 ; Mulaosmanovic et al., 2018a ).…”

Section: Introductionmentioning

confidence: 99%

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Section: Artificial Synapses Based On Emerging Materialsmentioning

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Section: Artificial Synapses Based On Emerging Materialsmentioning

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“…Refs. [30], [80] and [82] use the same calculation method, while ref. [21] and this work use the same calculation method.…”

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confidence: 99%

Oxide‐Based Electrolyte‐Gated Transistors for Spatiotemporal Information Processing

et al. 2020

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systems, which can be integrated with terminal sensors to form intelligent sensory systems. [3-7] Information processing in biological sensory nervous systems involves billions of neurons interconnected through trillions of synapses, constituting immense neural networks. [8] Compared with traditional von Neumannbased computing architecture, neural networks greatly reduce time-and energy consumption by taking the advantage of co-location of logic and memory, hyperconnectivity, robustness, and massively parallel processing. [9] As the third-generation artificial neural network, spiking neural networks (SNNs) are inspired by biological nervous systems. They employ spiking neurons as computational units that process information with timing of spikes. [10] Therefore, SNNs provide the potential for spatiotemporal information processing with high time-and energy-efficiency. The learning and recognition functions aiming at spatiotemporal patterns have been demonstrated in SNN formed by resistive switching synaptic devices. [11] However, this synaptic device suffers from several performance limitations such as the discrete weight update, write variation, and high energy programing related to the filamentary switching mechanism. [12-17] Recently, He et al. use capacitively coupled multi-terminal neurotransistors for spatiotemporal information processing, where the neurotransistors mimic the dendritic discriminability of different spatiotemporal input sequences. [18] To physically emulate neural networks in hardware, large-scale crossbar Spiking neural networks (SNNs) sharing large similarity with biological nervous systems are promising to process spatiotemporal information and can provide highly time-and energy-efficient computational paradigms for the Internet-of-Things and edge computing. Nonvolatile electrolyte-gated transistors (EGTs) provide prominent analog switching performance, the most critical feature of synaptic element, and have been recently demonstrated as a promising synaptic device. However, high performance, large-scale EGT arrays, and EGT application for spatiotemporal information processing in an SNN are yet to be demonstrated. Here, an oxide-based EGT employing amorphous Nb 2 O 5 and Li x SiO 2 is introduced as the channel and electrolyte gate materials, respectively, and integrated into a 32 × 32 EGT array. The engineered EGTs show a quasi-linear update, good endurance (10 6) and retention, a high switching speed of 100 ns, ultralow readout conductance (<100 nS), and ultralow areal switching energy density (20 fJ µm −2). The prominent analog switching performance is leveraged for hardware implementation of an SNN with the capability of spatiotemporal information processing, where spike sequences with different timings are able to be efficiently learned and recognized by the EGT array. Finally, this EGT-based spatiotemporal information processing is deployed to detect moving orientation in a tactile sensing system. These results provide an insight into oxide-based EGT devices for energy-efficient neuro...

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“…They show evidence that alloy nanoparticles-based devices have reproducible diffusive switching characteristics and offer a high design versatility to tune such switching properties. The form of the resistance state is crucial for the successful training of artificial neural networks; Nikam et al 6 present a Li ion synaptic transistor with high ionic conductivity that allows linear conductance switching with several discrete non-volatile states. Also in 7 the impact of Li-based devices for the emulation of synaptic behaviour is shown.…”

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confidence: 99%

Novel hardware and concepts for unconventional computing

Ziegler

2020

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neuromorphic systems are currently experiencing a rapid upswing due to the fact that today's cMoS (complementary metal oxide silicon) based technologies are increasingly approaching their limits. in particular, for the area of machine learning, energy consumption of today's electronics is an important limitation, that also contributes toward the ever-increasing impact of digitalization on our climate. thus, in order to better meet the special requirements of unconventional computing, new physical substrates for bio-inspired computing schemes are extensively exploited. the aim of this Guest edited collection is to provide a platform for interdisciplinary research along three main lines: memristive materials and devices, emulation of cellular learning (neurons and synapses), and unconventional computing and network schemes.

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Near ideal synaptic functionalities in Li ion synaptic transistor using Li3POxSex electrolyte with high ionic conductivity

Cited by 72 publications

References 31 publications

Emerging Materials for Neuromorphic Devices and Systems

Emerging Materials for Neuromorphic Devices and Systems

Oxide‐Based Electrolyte‐Gated Transistors for Spatiotemporal Information Processing

Novel hardware and concepts for unconventional computing

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