A new approach to modeling TiO2−x-based memristors using molecular dynamics simulation

Rajabiyoun, Niloufar; Karacalı, Tevhit

doi:10.1007/s00339-019-2602-0

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…Rajabiyoun and Karacali used the Buckingham model to explore oxygen ion migration in rutile titanium dioxide (TiO 2−x ) memristors with oxygen and titanium vacancies. 282 Such fixed charge models, however, are limited in their ability to capture charge dynamics and nonstoichiometric effects. On the other hand, models such as EAM and MEAM do not capture electrostatic interactions.…”

Section: Metals and Oxidesmentioning

confidence: 99%

“…The models described above have been primarily employed to study the mechanistic details of switching in memristor-based devices. Rajabiyoun and Karacali used the Buckingham model to explore oxygen ion migration in rutile titanium dioxide (TiO 2–x ) memristors with oxygen and titanium vacancies . Such fixed charge models, however, are limited in their ability to capture charge dynamics and nonstoichiometric effects.…”

Section: Theoretical Models To Study Proton-induced Neuromorphic Beha...mentioning

confidence: 99%

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Proton Conducting Neuromorphic Materials and Devices

Yuan,

Patel,

Banik

et al. 2024

Chem. Rev.

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Neuromorphic computing and artificial intelligence hardware generally aims to emulate features found in biological neural circuit components and to enable the development of energy-efficient machines. In the biological brain, ionic currents and temporal concentration gradients control information flow and storage. It is therefore of interest to examine materials and devices for neuromorphic computing wherein ionic and electronic currents can propagate. Protons being mobile under an external electric field offers a compelling avenue for facilitating biological functionalities in artificial synapses and neurons. In this review, we first highlight the interesting biological analog of protons as neurotransmitters in various animals. We then discuss the experimental approaches and mechanisms of proton doping in various classes of inorganic and organic proton-conducting materials for the advancement of neuromorphic architectures. Since hydrogen is among the lightest of elements, characterization in a solid matrix requires advanced techniques. We review powerful synchrotronbased spectroscopic techniques for characterizing hydrogen doping in various materials as well as complementary scattering techniques to detect hydrogen. First-principles calculations are then discussed as they help provide an understanding of proton migration and electronic structure modification. Outstanding scientific challenges to further our understanding of proton doping and its use in emerging neuromorphic electronics are pointed out.

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Section: Metals and Oxidesmentioning

confidence: 99%

Section: Theoretical Models To Study Proton-induced Neuromorphic Beha...mentioning

confidence: 99%

Proton Conducting Neuromorphic Materials and Devices

Yuan,

Patel,

Banik

et al. 2024

Chem. Rev.

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“…Similar MD simulations highlighting other aspects of memristive behavior and ion dynamics can be found in recent literature. [84][85] Access to longer time scales relevant for vacancy dynamics can be reached via kinetic Monte-Carlo (kMC) simulations. A kMC approach was used to investigate structural configurations of a TiO2 memristor, with a focus on oxygen vacancy migration under an externally applied voltage.…”

Section: Memfractors Memristors and Modelingmentioning

confidence: 99%

Towards synthetic neural networks: can artificial electrochemical neurons be coupled with artificial memristive synapses?

Wlaźlak

Przyczyna

Gutiérrez

et al. 2020

Jpn. J. Appl. Phys.

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The enormous amount of data generated nowadays worldwide is increasingly triggering the search for unconventional and more efficient ways of processing and classifying information, eventually able to transcend the conventional von-Neumann-Turing computational central dogma. It is, therefore, greatly appealing to draw inspiration from less conventional but computationally more powerful systems such as the neural architecture of the human brain.This neuromorphic route has the potential to become one of the most influential and long-lasting paradigms in the field of unconventional computing. The material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the above mentioned von-Neumann-Turing prescription; we do know, however, that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons. This requires challenging the intrinsic definition of the single units and the architecture of computing machines. Memristive and the recently proposed memfractive systems have been shown to display basic features of neural systems such as synaptic-like plasticity and memory features, so that they may offer a diverse playground to implement synaptic connections. Their combination with reservoir computing approaches can further increase their versatility since reservoir networks do not require extra optimization of internal connections. In this review, we address various material-based strategies of implementing unconventional computing hardware: (i) electrochemical oscillators based on liquid metals and (ii) mem-devices exploiting Schottky barrier modulation in polycrystalline and disordered structures made of oxide or perovskite-type semiconductors. Both items (i) and (ii) build the two pillars of neuromimetic computing devices, which we will denote as synthetic neural networks. We complement the more experimental aspects of the review with an overview of few atomistic and phenomenological modelling approaches of memdevices as well as of reservoir computing networks. We expect that the current review will be of great interest for scientists aiming at bridging unconventional computing strategies with specific materials-based platforms.

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“…Examination of prior research indicates that the physical features of nanomaterials can change under external conditions, and BAs are no exception to this rule [24][25][26][27][28][29][30]. Doping [24,25,[31][32][33], applying strain [26,27], variations in temperature [28], exposure to radiation [34], defects [35][36][37][38], and subjecting materials to electric [29] and magnetic [30] fields are among the primary factors that significantly influence nanomaterial properties. These factors can be utilized to alter the properties of nanomaterials and achieve novel nanostructures.…”

Section: Introductionmentioning

confidence: 99%

Electronic and optical characteristics of phosphorus-doped two-dimensional hexagonal boron arsenide: the effects of doping concentration and mechanical strain

Ertekin

2024

Phys. Scr.

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The present study investigates the impact of P doping and stretching loads on phonon dispersion, electronic properties, and optical characteristics of P-doped hexagonal boron arsenide (h-BAs(1-x)Px), where the doping level x varies from 0 to 1, employing the density functional theory (DFT) method. The findings reveal that the chemical bonds in h-BAs(1-x)Px monolayers are indeed covalent. Furthermore, an increase in P concentration from 0.0% to 100% leads to enhancement in the band gap, approximately 18.42%. However, regardless of variations in P concentration or the application of tensile strains up to 4%, the electronic nature of h-BAs(1-x)Px remains unaltered. These monolayers continue to exhibit characteristics of a direct band gap semiconductor at the K wave vector. On the other hand, there exists an intricate interplay between strain and optical properties. Investigating the dielectric functions, absorption coefficient, refractive index, and reflectivity coefficient of h-BAs(1-x)Px monolayers provides insights into their behavior in the ultraviolet spectrum.

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A new approach to modeling TiO2−x-based memristors using molecular dynamics simulation

Cited by 6 publications

References 29 publications

Proton Conducting Neuromorphic Materials and Devices

Proton Conducting Neuromorphic Materials and Devices

Towards synthetic neural networks: can artificial electrochemical neurons be coupled with artificial memristive synapses?

Electronic and optical characteristics of phosphorus-doped two-dimensional hexagonal boron arsenide: the effects of doping concentration and mechanical strain

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