Efficient two-terminal artificial synapse based on a network of functionalized conducting polymer nanowires

High-density memory devices are essential to sustain growth in information technology (IT). Furthermore, brain-inspired computing devices are the future of IT businesses such as artificial intelligence, deep learning, and big data. Herein, we propose a facile and hierarchical nickel cobaltite (NCO) quasi-hexagonal nanosheet-based memristive device for multilevel resistive switching (RS) and synaptic learning applications. Electrical measurements of the Pt/NCO/Pt device show the electroforming free pinched hysteresis loops at different voltages, suggesting the multilevel RS capability of the device. The detailed memristive properties of the device were calculated using the time-dependent current–voltage data. The two-valued charge-flux properties indicate the memristive and multilevel RS characteristics of the device. Interestingly, the Pt/NCO/Pt memristive device shows a compliance current (CC)-dependent RS property; compliance-free RS was observed from 10−2 to 10−4 A, and the compliance effect dominated in the range of 10−5–10−6 A. In CC control mode, the device demonstrated three resistance states during endurance and retention measurements. In addition, the device was successful in mimicking biological synaptic properties such as potentiation-depression- and spike-timing-dependent plasticity rules. The results of the present investigation demonstrated that solution-processable NCO nanosheets are potential switching materials for high-density memory and brain-inspired computing applications.

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“…8e). The opposite behavior was observed for SAH learning by simply changing the polarity of the spike 58 (Fig. 8f).…”

Section: Resultsmentioning

confidence: 79%

Facile synthesis of nickel cobaltite quasi-hexagonal nanosheets for multilevel resistive switching and synaptic learning applications

et al. 2021

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“…STDP is an extension of Hebbian learning rule that refers to adjust synaptic weights according to the interval and order of connected neurons spikes. [ 9,50–54 ] Stronger connection between pre‐ and post‐neuron can be attained with the shorter interval of their spikes, leading to a greater response in synaptic weight. In antisymmetric Hebbian, the arrival order of the pre‐ and post‐neuron spikes determine the process of LTP or LTD.…”

Section: Resultsmentioning

confidence: 99%

“…Besides, for the symmetric STDP function mimicked in our synaptic transistor (Figure 5c), only LTP process occur and the ∆W% only depends on the interval, not on the order between pre‐ and post‐neuron spikes. Furthermore, the experimental data also can be fitted with the anti‐Hebbian learning rules, as the Gaussian function shows: [ 54 ]

Δ W = W_{0} + A \exp (- \frac{normalΔ t^{2}}{τ^{2}})

…”

Section: Resultsmentioning

confidence: 99%

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Deng

Wang

Liu

et al. 2021

Adv Funct Materials

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114

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Designing transparent flexible electronics with multi‐biological neuronal functions and superior flexibility is a key step to establish wearable artificial intelligence equipment. Here, a flexible ionic gel‐gated VO2 Mott transistor is developed to simulate the functions of the biological synapse. Short‐term and long‐term plasticity of the synapse are realized by the volatile electrostatic carrier accumulation and nonvolatile proton‐doping modulation, respectively. With the achievement of multi‐essential synaptic functions, an important sensory neuron, nociceptor, is perfectly simulated in our synaptic transistors with all key characteristics of threshold, relaxation, and sensitization. More importantly, this synaptic transistor exhibits high tolerance to the bending deformation, and the cycle‐to‐cycle variations of multi‐conductance states in potentiation and depression properties are maintained within 4%. This superior stability further indicates that our flexible device is suitable for neuromorphic computing. Simulation results demonstrate that high recognition accuracy of handwritten digits (>95%) can be achieved in a convolution neural network built from these synaptic transistors. The transparent and flexible Mott transistor based on electrically‐controlled VO2 metal‐insulator transition is believed to open up alternative approaches to developing highly stable synapses for future flexible neuromorphic systems.

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“…Electrochemical macromolecular motors from conducting polymers, carbon nanotubes, graphenes and other electroactive materials act as transducers between thermal, electrical, chemical and mechanical energies. The way is open now to translate those concepts attained form the temperature inuence on both, the material reactions and the articial muscles actuation to increase either, the energetic efficiencies, the transition times and the amplitude of the transient magnitude of any other electrical or electrochemical device working by cooperative actuation of macromolecular electrochemical motors: batteries and super-capacitors, [107][108][109][110][111][112][113][114][115][116][117][118] smart windows, 116,[119][120][121][122][123][124] smart membranes, [125][126][127][128][129][130][131][132][133][134][135][136] nervous interfaces or articial chemical synapse, 134,[137][138][139][140] smart skins, smart drug delivery, 141-146 ionic trapping, 32,33,147 and so on.…”

Section: Technological Perspectivesmentioning

confidence: 99%

Electroactive macromolecular motors as model materials of ectotherm muscles

Otero

2021

RSC Adv.

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Efficient two-terminal artificial synapse based on a network of functionalized conducting polymer nanowires

Abstract: A two-terminal artificial synaptic device based on functionalized polythiophene nanowires network is fabricated and successfully used to emulate important biological synaptic functions, including PPF, SRDP and STDP.

Cited by 39 publications

References 46 publications

Facile synthesis of nickel cobaltite quasi-hexagonal nanosheets for multilevel resistive switching and synaptic learning applications

Facile synthesis of nickel cobaltite quasi-hexagonal nanosheets for multilevel resistive switching and synaptic learning applications

A Flexible Mott Synaptic Transistor for Nociceptor Simulation and Neuromorphic Computing

Electroactive macromolecular motors as model materials of ectotherm muscles

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