Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications

Emelyanov, A. V.; Nikiruy, K. E.; Демин, В. А.; Rylkov, V. V.; Belov, A. I.; Королев, Д. С.; Gryaznov, E.G.; Павлов, Д. А.; Горшков, О. Н.; Mikhaylov, A. N.; Dimitrakis, P.

doi:10.1016/j.mee.2019.110988

Cited by 63 publications

(33 citation statements)

References 36 publications

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“…Since the first experiments and simulations (Linares-Barranco et al, 2011), significant progress has been achieved in the implementation of excitatory and inhibitory STDP by using resistive-switching devices (RRAM), which are a particular class of memristors with two-terminal metal-insulator-metal structure. Although most of STDP demonstrations still rely on a time overlap of preand postsynaptic spikes (Yu et al, 2011;Kuzum et al, 2013;Emelyanov et al, 2019), the rich internal dynamics of higherorder memristive devices related to multi-time-scale microscopic transport phenomena provides timing-and frequency-dependent plasticity in response to non-overlapping input signals in a biorealistic fashion (Du et al, 2015;Kim et al, 2015). Memristive plasticity can be realized at different time scales, in particular with STDP windows of the order of microseconds (Kim et al, 2015), which is essential for the development of fast spike encoding systems.…”

Section: Discussionmentioning

confidence: 99%

“…However, more sophisticated architectures are required to reproduce different types of associative learning to be adopted in advanced robotic systems. We anticipate that, soon, artificial neurons can be realized on the CMOS architecture, whereas the STDP can be implemented by incorporating memristors (Emelyanov et al, 2019). It seems convenient to have paired micro-scaled memristive devices to reproduce bipolar synaptic weights.…”

Section: Discussionmentioning

confidence: 99%

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Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

et al. 2020

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Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a "living computer" based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

et al. 2020

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“…In the latter case, bilayer or multilayer structures are formed, in which the switching oxide alternates with a barrier/buffer layer (layers) to control the migration of oxygen vacancies, with a layer of low dielectric constant to obtain nonlinear current–voltage ( I – V ) characteristics, or with a layer of higher/lower thermal conductivity for the removal/retention of heat in the switching area and to achieve analog switching character. To tune the resistive states with given accuracy, regardless of their native variation, adaptive programming of resistive state is actively employed by correcting the parameters of switching voltage pulses depending on the result of programming (so called write‐and‐verify approach). All these approaches complicates the technological process for the fabrication of memristive devices and control circuits, but have not yet lost their relevance in demonstrating prototypes of functional devices based on memristors.…”

Section: Introductionmentioning

confidence: 99%

Multilayer Metal‐Oxide Memristive Device with Stabilized Resistive Switching

Mikhaylov

Belov

Королев

et al. 2019

Adv Materials Technologies

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nanoelectronic devices with the effect of resistive switching, which consists in a reversible change of resistance in response to electrical stimulation [5] and is identified with the memristive effect. [6] Despite the significant progress in understanding of the memristive effect and approaching maturity of the technology of resistiveswitching devices over the last 10 years, there are still a number of fundamental problems to solve.A key problem on the way of using resistive-switching devices as programmable elements in memory devices and mixed analog-digital processors of new generation is the variability of resistive switching parameters inherent to memristive thin-film devices. [7] Achieving stable switching between the nonlinear resistive states is also an important task on the way to implementing large passive crossbar arrays of memristors and solving the problem of leakage currents in them. [8,9] Metal-oxide memristive devices are most compatible with the traditional complementary metal oxide semiconductor (CMOS) process and exhibit a valence change memory effect. [10] The variation of switching parameters in such devices is caused by the stochastic nature of migration of oxygen ions and/or vacancies responsible for the local oxidation and recovery of conductive channels (filaments) and is accompanied by the degradation of switching parameters in the case of uncontrolled oxygen exchange between the dielectric and electrode materials.The traditional approaches to control the reproducibility of resistive switching include the formation of special electric field concentrators [11][12][13] and appropriate selection of materials/interfaces in memristive device structure. In the latter case, bilayer or multilayer structures are formed, in which the switching oxide alternates with a barrier/buffer layer (layers) to control the migration of oxygen vacancies, [14,15] with a layer of low dielectric constant [16,17] to obtain nonlinear currentvoltage (I-V) characteristics, or with a layer of higher/lower thermal conductivity [18,19] for the removal/retention of heat in the switching area and to achieve analog switching character. To tune the resistive states with given accuracy, regardless of

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“…Recent studies have shown the possibility of rate and temporal coding in SNN using a combination of Hebbian learning (through triplet-based STDP), synaptic and neuronal competition (Lobov et al, 2020a,b). Hebbian and other STDP rules have been demonstrated for a large number of different kinds of memristors (Kim et al, 2015;Ielmini and Waser, 2016;Emelyanov et al, 2019;Minnekhanov et al, 2019) that confirms their high potential to serve as the self-adjusting weights between neurons in SNN.…”

Section: Memristive Neural Arcitectures: Toward Neuroprostheticsmentioning

confidence: 78%

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

et al. 2020

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Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale crossbar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a newgeneration robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

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Yttria-stabilized zirconia cross-point memristive devices for neuromorphic applications

Cited by 63 publications

References 36 publications

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot

Multilayer Metal‐Oxide Memristive Device with Stabilized Resistive Switching

Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics

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