On the resistive switching mechanism of parylene-based memristive devices

Миннеханов, А. А.; Shvetsov, Boris S.; Martyshov, M. N.; Nikiruy, K. E.; Кукуева, Е. В.; Presnyakov, M. Yu.; Форш, П. А.; Rylkov, V. V.; Erokhin, Victor; Демин, В. А.; Emelyanov, A. V.

doi:10.1016/j.orgel.2019.06.052

Cited by 58 publications

(40 citation statements)

References 48 publications

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“…Such a mechanism is realized in different oxides (TiO 2 , SiO 2 , HfO 2 , etc. ), [11][12][13][14][15] with filaments composed of oxygen vacancies, or in dielectrics (SiO 2 , organic materials), [16][17][18] with metal bridges formed by cations. In this case, the variability of parameters, both device to device and cycle to cycle, could be significant because of the random character of the filament (bridge) formation process.…”

Section: Introductionmentioning

confidence: 99%

Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Nikiruy

Surazhevsky

Демин

et al. 2020

Physica Status Solidi (a)

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In hardware neuromorphic systems (NSs), memristors are used as synaptic connections. In such systems, spike‐timing‐dependent plasticity (STDP) is a promising local learning rule. Herein, STDP is studied in a system composed of a pair of hardware or software neurons connected by a (CoFeB)x(LiNbO3)100−x nanocomposite‐based memristor. The dopamine‐like modulation of memristor‐based STDP is implemented simply by the change in the polarity of spikes generated by artificial neurons operating in the inhibitory or excitatory mode. This modulation method is shown to be compatible with hardware neurons, different spike shapes, and is used in spiking NSs with bioinspired dopamine‐like reinforcement learning.

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Section: Introductionmentioning

confidence: 99%

Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Nikiruy

Surazhevsky

Демин

et al. 2020

Physica Status Solidi (a)

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“…Квантование проводимости наблюдается в структурах Cu/PPX/ITO, особенно при РП в низкоомное состояние, но механизм полуцелого квантования остается неясным. Также остается открытым вопрос, почему некоторые уровни проводимости (резистивные состояния) менее стабильны, что наблюдалось и ранее в экспериментах с этими структурами [17,26]. Относительно природы наблюдаемых полуцелых квантов проводимости в научном сообществе на сегодняшний день единой позиции нет.…”

Section: результаты и обсуждениеunclassified

Квантование Проводимости В Мемристивных Структурах На Основе Поли-П-Ксилилена

Швецов¹,

Миннеханов²,

Несмелов³

et al. 2020

Физика И Техника Полупроводников

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The paper presents the results of a study at room temperature of the quantization effect of the conductivity of memristive structures based on the organic material poly-p-xylylene with resistive switching. Measurement methods are shown and a comparative analysis of the manifestation of the effect when switching structures to a high-resistance and low-resistance state is carried out. The possibility of setting stable quantum states of conductivity in memristive structures based on poly-p-xylylene is demonstrated. It is shown that some of these states have short-term, and some long-term stability. The results obtained open up new possibilities for using the quantization effect of conductivity in the implementation of neuromorphic systems.

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“…Spiking networks with the plasticity approximating nanocomposite (NC) memristors (CoFeB) x (LiNbO 3 ) 1−x were shown to classify the MNIST handwritten digits [15]. Recently, a highly-plastic poly-p-xylylene (PPX) memristor was created [16], which makes it relevant to study the possibility of learning about SNNs, with plasticity modeling that type of memristor.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks

et al. 2021

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The problem with training spiking neural networks (SNNs) is relevant due to the ultra-low power consumption these networks could exhibit when implemented in neuromorphic hardware. The ongoing progress in the fabrication of memristors, a prospective basis for analogue synapses, gives relevance to studying the possibility of SNN learning on the base of synaptic plasticity models, obtained by fitting the experimental measurements of the memristor conductance change. The dynamics of memristor conductances is (necessarily) nonlinear, because conductance changes depend on the spike timings, which neurons emit in an all-or-none fashion. The ability to solve classification tasks was previously shown for spiking network models based on the bio-inspired local learning mechanism of spike-timing-dependent plasticity (STDP), as well as with the plasticity that models the conductance change of nanocomposite (NC) memristors. Input data were presented to the network encoded into the intensities of Poisson input spike sequences. This work considers another approach for encoding input data into input spike sequences presented to the network: temporal encoding, in which an input vector is transformed into relative timing of individual input spikes. Since temporal encoding uses fewer input spikes, the processing of each input vector by the network can be faster and more energy-efficient. The aim of the current work is to show the applicability of temporal encoding to training spiking networks with three synaptic plasticity models: STDP, NC memristor approximation, and PPX memristor approximation. We assess the accuracy of the proposed approach on several benchmark classification tasks: Fisher’s Iris, Wisconsin breast cancer, and the pole balancing task (CartPole). The accuracies achieved by SNN with memristor plasticity and conventional STDP are comparable and are on par with classic machine learning approaches.

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On the resistive switching mechanism of parylene-based memristive devices

Cited by 58 publications

References 48 publications

Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Spike‐Timing‐Dependent and Spike‐Shape‐Independent Plasticities with Dopamine‐Like Modulation in Nanocomposite Memristive Synapses

Квантование Проводимости В Мемристивных Структурах На Основе Поли-П-Ксилилена

Modeling the Dynamics of Spiking Networks with Memristor-Based STDP to Solve Classification Tasks

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