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This study investigates 7-methylquinolinium halobismuthates (I, Br, and Cl) in two aspects: (1) their structural and semiconducting properties influenced by anionic composition, and (2) their memristive and plasticity characteristics for…

This study investigates the impacts of structure and composition on the optical and electronic properties of a series of pyridinium-based bismuth iodide complexes. Organic substrates with various functional groups, such as 4-aminopyridine…

The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS…

Emerging Technologies · Computer Science 2016-04-25 Shari Lim Wei , Eleni Vasilaki , Ali Khiat , Iulia Salaoru , Radu Berdan , Themistoklis Prodromakis

Nanoscale metal oxide memristors have potential in the development of brain-inspired computing systems that are scalable and efficient1-3. In such systems, memristors represent the native electronic analogues of the biological synapses.…

Mesoscale and Nanoscale Physics · Physics 2016-12-21 Cheng-Chih Hsieh , Anupam Roy , Yao-Feng Chang , Davood Shahrjerdi , Sanjay K. Banerjee

Memristors that mimic brain functions are crucial for energy-efficient neuromorphic devices. Ion channels that emulate biological synapses are still in the early stages of development, especially the tunability of memory states. Here, we…

Materials Science · Physics 2024-12-09 Dhal Biswabhusan , Puzari Animesh , Li-Hsien Yeh , Kalon Gopinadhan

Dynamic reconfiguration of charge carriers in confined ion-channels under electrical stimulation produces memory effects, where the internal resistance depends on history of the electric field. Vermiculite nanofluidic devices harness this…

Currently, memristor devices suffer from variability between devices and from cycle to cycle. In this work, we study the impact of device variations on memristive Material Implication (IMPLY). New constraints for different parameters and…

Emerging Technologies · Computer Science 2021-01-19 Simon Michael Laube , Nima TaheriNejad

Metal-oxide memristors have emerged as promising candidates for hardware implementation of artificial synapses - the key components of high-performance, analog neuromorphic networks - due to their excellent scaling prospects. Since some…

Other Condensed Matter · Physics 2016-10-10 M. Prezioso , F. Merrikh-Bayat , B. Hoskins , K. Likharev , D. Strukov

The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was studied. Epileptiform neuronal activity was obtained in vitro in the hippocampus slices of…

The electrical properties and performance characteristics of niobium dioxide (NbO$_\mathrm{2}$)-based memristive devices are examined at cryogenic temperatures. Sub-stoichiometric Nb$_\mathrm{2}$O$_\mathrm{5}$ was deposited via magnetron…

The memristor can be used as non volatile memory (NVM) and for emulating neuron behavior. It has the ability to switch between low resistance $R_{on}$ and high resistance values $R_{off}$, and exhibit the synaptic dynamic behaviour such as…

Emerging Technologies · Computer Science 2018-09-28 Aidana Irmanova , Grant A. Ellis , Alex Pappachen James

Nanofluidic memristors have demonstrated great potential for neuromorphic system applications with the advantages of low energy consumption and excellent biocompatibility. Here, an effective way is developed to regulate the memristive…

Chemical Physics · Physics 2025-10-21 Zhe Liu , Hongwen Zhang , Di Liu , Tianyi Sui , Yinghua Qiu

Memristive materials are related to neuromorphic applications as they can combine information processing with memory storage in a single computational element, just as biological neurons. Many of these bioinspired materials emulate the…

It is now widely accepted that memristive devices are perfect candidates for the emulation of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive…

Neural and Evolutionary Computing · Computer Science 2012-11-26 Farnood Merrikh-Bayat , Saeed Bagheri Shouraki , Iman Esmaili Paeen Afrakoti

We extend the notion of memristive systems to capacitive and inductive elements, namely capacitors and inductors whose properties depend on the state and history of the system. All these elements show pinched hysteretic loops in the two…

Mesoscale and Nanoscale Physics · Physics 2009-11-21 Massimiliano Di Ventra , Yuriy V. Pershin , Leon O. Chua

Memristive devices are promising elements for energy-efficient neuromorphic computing and future artificial intelligence systems. For diffusive memristors, the device state switching occurs because of the sequential formation and…

Materials Science · Physics 2022-02-14 D. P Pattnaik , Y. Ushakov , Z. Zhou , P. Borisov , M. D Cropper , U. W. Wijayantha , A. G. Balanov , S. E Savel'ev

Memristive circuit elements constitute a cornerstone for novel electronic applications, such as neuromorphic computing, called to revolutionize information technologies. By definition, memristors are sensitive to the history of electrical…

We present a computationally inexpensive yet accurate phenomenological model of memristive behavior in titanium dioxide devices by fitting experimental data. By design, the model predicts most accurately I-V relation at small non-disturbing…

Materials Science · Physics 2015-06-22 Farnood Merrikh-Bayat , Brian Hoskins , Dmitri B. Strukov

Using molecular dynamics simulations, we show that, when subject to a periodic external electric field, a nanopore in ionic solution acts as a capacitor with memory (memcapacitor) at various frequencies and strengths of the electric field.…

Soft Condensed Matter · Physics 2010-07-20 Matt Krems , Yuriy V. Pershin , Massimiliano Di Ventra

There are three theoretical models which purport to relate experimentally-measurable or fabrication-controllable device properties to the memristor's operation: 1. Strukov et al's phenomenological model; 2. Georgiou et al's Bernoulli…

Materials Science · Physics 2013-12-17 Ella M. Gale , Benjamin de Lacy Costello , Andrew Adamatzky
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