Stochastic resonance in a metal-oxide memristive device

Mikhaylov, A. N.; Гусейнов, Д. В.; Belov, A. I.; Королев, Д. С.; Shishmakova, V.A.; Koryazhkina, M. N.; Филатов, Д. О.; Горшков, О. Н.; Maldonado, David; Alonso, Francisco; Roldán, J.B.; Krichigin, A. V.; Agudov, N. V.; Dubkov, A. A.; Carollo, Angelo; Spagnolo, Bernardo

doi:10.1016/j.chaos.2021.110723

Cited by 113 publications

(44 citation statements)

References 52 publications

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“…To address these issues, inserting an interlayer [ 20 , 21 ], which can function as an oxygen reservoir and possibly controls oxygen ions concentration in the original and inserted layers, thereby improving resistive switching parameters [ 22 ], is conducive in enhancing resistive switching performance. In transition metal oxides-based memristive devices, oxygen ions movement and their distribution usually play a substantial role in resistive switching behavior, which is now widely accepted [ 23 , 24 ]. In a previous work, we have shown that by inserting a thin layer (such as ZTO, CeO 2 , TiO 2 , and HfO 2 ) to remove resistive switching parameters instability and their dispersion is a practical and effective method [ 20 , 21 , 25 – 27 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

et al. 2022

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For neuromorphic computing and high-density data storage memory, memristive devices have recently gained a lot of interest. So far, memristive devices have suffered from switching parameter instability, such as distortions in resistance values of low- and high-resistance states (LRSs and HRSs), dispersion in working voltage (set and reset voltages), and a small ratio of high and low resistance, among other issues. In this context, interface engineering is a critical technique for addressing the variation issues that obstruct the use of memristive devices. Herein, we engineered a high band gap, low Gibbs free energy Al2O3 interlayer between the HfO2 switching layer and the tantalum oxy-nitride electrode (TaN) bottom electrode to operate as an oxygen reservoir, increasing the resistance ratio between HRS and LRS and enabling multilayer data storage. The Pt/HfO2/Al2O3/TaN memristive device demonstrates analog bipolar resistive switching behavior with a potential ratio of HRS and LRS of > 105 and the ability to store multi-level data with consistent retention and uniformity. On set and reset voltages, statistical analysis is used; the mean values (µ) of set and reset voltages are determined to be − 2.7 V and + 1.9 V, respectively. There is a repeatable durability over DC 1000 cycles, 105 AC cycles, and a retention time of 104 s at room temperature. Quantum conductance was obtained by increasing the reset voltage with step of 0.005 V with delay time of 0.1 s. Memristive device has also displayed synaptic properties like as potentiation/depression and paired-pulse facilitation (PPF). Results show that engineering of interlayer is an effective approach to improve the uniformity, ratio of high and low resistance, and multiple conductance quantization states and paves the way for research into neuromorphic synapses.

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Section: Introductionmentioning

confidence: 99%

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

et al. 2022

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“…Stochasticity is an intrinsic property of a memristor ( Carboni and Ielmini, 2019 ). Noise can be used both to study the multistable nature and to control the behavior, thanks to such well-known effects as stochastic resonance and enhancement of the stability of metastable states ( Mikhaylov et al, 2021 ), resonant activation ( Ryabova et al, 2021 ), etc. These and other phenomena related to the constructive role of noise can be described within the framework of analytical stochastic models ( Agudov et al, 2020 , 2021 ).…”

Section: The Memristive Architecture Enables the Implementation Of Re...mentioning

confidence: 99%

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Makarov

Lobov

Shchanikov

et al. 2022

Front. Comput. Neurosci.

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The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.

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“…The main disadvantages of memristors fabricated in the form of metal-insulator-metal (MIM) or metal-insulator-semiconductor (MIS) structures are the reproducibility of resistive switching (RS) parameters, which is insufficient for practical use (stochasticity), high values of RS voltages, and the complexity of integration into a standard complementary metal-oxide-semiconductor (CMOS) fabrication process. Currently, approaches to solving these problems are being developed: the use of new materials and various interfaces [13][14][15][16], the use of signals with a special shape for RS [17], the use of optical radiation [18] or noise [19][20][21] as parameters controlling the switching dynamics, programming the amplitudes and durations of switching pulses [22,23], etc. Indeed, the wider application of memristors is limited by their insufficient stability, the high variability of RS parameters, and a lack of understanding of the drift-diffusion processes responsible [24].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the wider application of memristors is limited by their insufficient stability, the high variability of RS parameters, and a lack of understanding of the drift-diffusion processes responsible [24]. One of the fundamental origins of the instability of the memristor parameters is the essentially stochastic nature of RS [20]. Furthermore, the noise sources can induce new ordered dynamical structures and cause new phase transition phenomena [25,26].…”

Section: Introductionmentioning

confidence: 99%

Silicon-Compatible Memristive Devices Tailored by Laser and Thermal Treatments

Koryazhkina

Филатов

Тихов

et al. 2022

JLPEA

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Nowadays, memristors are of considerable interest to researchers and engineers due to the promise they hold for the creation of power-efficient memristor-based information or computing systems. In particular, this refers to memristive devices based on the resistive switching phenomenon, which in most cases are fabricated in the form of metal–insulator–metal structures. At the same time, the demand for compatibility with the standard fabrication process of complementary metal–oxide semiconductors makes it relevant from a practical point of view to fabricate memristive devices directly on a silicon or SOI (silicon on insulator) substrate. Here we have investigated the electrical characteristics and resistive switching of SiOx- and SiNx-based memristors fabricated on SOI substrates and subjected to additional laser treatment and thermal treatment. The investigated memristors do not require electroforming and demonstrate a synaptic type of resistive switching. It is found that the parameters of resistive switching of SiOx- and SiNx-based memristors on SOI substrates are remarkably improved. In particular, the laser treatment gives rise to a significant increase in the hysteresis loop in I–V curves of SiNx-based memristors. Moreover, for SiOx-based memristors, the thermal treatment used after the laser treatment produces a notable decrease in the resistive switching voltage.

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Stochastic resonance in a metal-oxide memristive device

Cited by 113 publications

References 52 publications

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

Robust Resistive Switching Constancy and Quantum Conductance in High-k Dielectric-Based Memristor for Neuromorphic Engineering

Toward Reflective Spiking Neural Networks Exploiting Memristive Devices

Silicon-Compatible Memristive Devices Tailored by Laser and Thermal Treatments

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