Flexible memristive devices with a structure of Al/polyimide:mica/poly(3,4-ethylenedioxythiophene) polystyrene sulfonate/indium-tin-oxide/polyethylene glycol naphthalate showed electrical bistability characteristics. The maximum current margin of the devices with mica nanosheets was much larger than that of the devices without mica nanosheets. For these devices, the current vs. time curves showed nonvolatile characteristics with a retention time of more than 1 × 10 s, and the current vs. number-of-cycles curves demonstrated an endurance for high resistance state/low resistance state switchings of 1 × 10 cycles. As to the operation performance, the "reset" voltage was distributed between 2.5 and 3 V, and the "set" voltage was distributed between -0.7 and -0.5 V, indicative of high uniformity. The electrical characteristics of the devices after full bendings with various radii of curvature were similar to those before bending, which was indicative of devices having ultra-flexibility. The carrier transport and the operation mechanisms of the devices were explained based on the current vs. voltage curves and the energy band diagrams.
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http://dx.doi.org/10.1038/s41598-018-30771-5 | DOI Listing |
Nano Lett
January 2025
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China.
Brain neural networks intricately integrate excitatory and inhibitory synaptic potentials to modulate the generation or suppression of action potentials, laying the foundation for neuronal computation. Although bioinspired nanofluidic systems have replicated some synaptic functions, complete integration of postsynaptic potentials remains unachieved. In this work, the developed ion concentration gradient nanofluidic memristor (ICGNM) modulates memristive effects through ion concentration gradient adjustments and exhibits synaptic plasticity phenomena, including paired-pulse facilitation, paired-pulse depression, and spike-rate-dependent plasticity.
View Article and Find Full Text PDFACS Nano
January 2025
Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore.
Two-dimensional (2D) materials hold significant potential for the development of neuromorphic computing architectures owing to their exceptional electrical tunability, mechanical flexibility, and compatibility with heterointegration. However, the practical implementation of 2D memristors in neuromorphic computing is often hindered by the challenges of simultaneously achieving low latency and low energy consumption. Here, we demonstrate memristors based on 2D cobalt phosphorus trisulfide (CoPS), which achieve impressive performance metrics including high switching speed (20 ns), low switching energy (1.
View Article and Find Full Text PDFACS Nano
January 2025
Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan.
Flexible and wearable electronics are experiencing rapid growth due to the increasing demand for multifunctional, lightweight, and portable devices. However, the growing demands of interactive applications driven by the rise of AI reveal the inadequate connectivity of current connection technologies. In this work, we successfully leverage memristive technology to develop a flexible radio frequency (RF) switch, optimized for 6G-compatible communication systems and adaptable to flexible applications.
View Article and Find Full Text PDFNanoscale
December 2024
Electronic Materials Laboratory, K. N. Toosi University of Technology, Tehran 1631714191, Iran.
Multibit/analog artificial synapses are in demand for neuromorphic computing systems. A problem hindering the utilization of memristive artificial synapses in commercial neuromorphic systems is the rigidity of their functional parameters, plasticity in particular. Here, we report fabricating polycrystalline rutile-based memristive memory segments with Ti/poly-TiO/Ti structures featuring multibit/analog storage and the first use of a tunable DC-biasing for synaptic plasticity adjustment from short- to long-term.
View Article and Find Full Text PDFNeural Netw
March 2025
School of Mathematical Science, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address:
This article is concerned with the deterministic finite automaton-mode-dependent (DFAMD) exponential stability problem of impulsive switched memristive neural networks (SMNNs) with aperiodic asynchronous attacks and the network covert channel. First, unlike the existing literature on SMNNs, this article focuses on DFA to drive mode switching, which facilitates precise system behavior modeling based on deterministic rules and input characters. To eliminate the periodicity and consistency constraints of traditional attacks, this article presents the multichannel aperiodic asynchronous denial-of-service (DoS) attacks, allowing for the diversity of attack sequences.
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