Possible $$D^{(*)}\bar{D}^{(*)}$$ D ( ∗ ) D ¯ ( ∗ ) and $$B^{(*)}\bar{B}^{(*)}$$ B ( ∗ ) B ¯ ( ∗ ) molecular states in the extended constituent quark models

Rao

Midya

et al. 2017

Adv Funct Materials

278

233

Threshold switches with Ag or Cu active metal species are volatile memristors (also termed diffusive memristors) featuring spontaneous rupture of conduction channels. The temporal dynamics of the conductance evolution is closely related to the electrochemical and diffusive dynamics of the active metals which could be modulated by electric field strength, biasing duration, temperature, and so on. Microscopic pictures by electron microscopy and quantitative thermodynamics modeling are examined to give insights into the underlying physics of the switching. Depending on the time scale of the relaxation process, such devices find a variety of novel applications in electronics, ranging from selector devices for memories to synaptic devices for neuromorphic computing.is applied due to the formation of a conduction channel(s) with Ag or Cu atoms. Unlike the ECM cells, the resistance recovers back spontaneously upon cessation of the external bias, yielding a superior I-V nonlinearity [4][5][6][7][8][9][10][11] and unique temporal conductance evolution dynamics. [7,12,13] Such a relaxation process is due to the physical dissolution of the metallic conduction channel under driving forces such as minimization of interfacial energy. In case active metals are used as electrodes, these metals may be doped into the dielectrics eventually under the combined effect of electric fields, thermal diffusion, which may lead to a reduced threshold voltage for the subsequent switching, similar to the process called "electroforming." The unique delay and relaxation dynamics of Ag and Cu-based threshold switches make them suitable for innovative applications in circuits and systems.Threshold switches with Ag or Cu active metals are also termed as "diffusive memristors" [7] to emphasize the underlying nature of the diffusive dynamics of the metal species. Factors including bias amplitude, biasing duration, as well as ambient temperature have been observed to have an impact on such a process, showing a wide range of dynamical properties, which could be exploited as access devices for memories with fast transition (e.g., <100 ns) or synaptic emulators with a relatively slower evolution (e.g., >1 µs). We survey the recently developed material systems which have exhibited this kind of threshold switching. New evidences by electron microscopy and quantitatively thermodynamic modeling are examined to give insights into the underlying physics of the mechanisms. We also discuss applications enabled by the advent of such threshold switches. Temporal Response of the SwitchingThe dynamical response of threshold switching is a critical property for many applications but has been characterized to a lesser extent. The temporal responses could be probed by applying voltage pulses and measuring the resulting currents in the time domain. It is a general observation in both ECM and threshold switches that the conductance experienced a transition from insulating state to conducting state after a finite time duration (delay time) under the external bias, as ill...

Section: Artificial Synapsesmentioning

confidence: 99%

Threshold Switching of Ag or Cu in Dielectrics: Materials, Mechanism, and Applications

Rao

Midya

et al. 2017

Adv Funct Materials

278

233

“…In the current research, GO‐g‐PAA was employed as nanofiller to fabricate nanocomposite hydrogels. GO‐g‐PAA was prepared by ceric salt initiated surface graft polymerization of AA onto GO according to the literature . GO and GO‐g‐PAA were characterized by FTIR, TGA, and TEM.…”

Section: Resultsmentioning

confidence: 99%

“…First, GO‐g‐PAA was prepared by ceric salt initiated surface graft polymerization of AA onto GO according to the method in literature . Typically, desired amount of GO was dispersed into water by sonication in ice‐water bath to get 100 mL of 0.1 wt % aqueous suspension, then 6 g acrylic acid was added and mixed.…”

Section: Methodsmentioning

confidence: 99%

Fabrication of tough, self‐recoverable, and electrically conductive hydrogels byin situreduction of poly(acrylic acid) grafted graphene oxide in polyacrylamide hydrogel matrix

J of Applied Polymer Sci

et al. 2019

Developing electrically conductive hydrogels with good electronic properties and excellent mechanical performance is significant to their potential applications. In this article, we present a strategy to fabricate tough, self‐recoverable and electrically conductive hydrogels containing reduced graphene oxide (rGO). Poly(acrylic acid) grafted graphene oxide (GO‐g‐PAA) was synthesized and incorporated into chemically crosslinked polyacrylamide (PAM) networks to obtain GO‐g‐PAA/PAM hydrogels, which were further treated with ascorbic acid solution at ambient temperature to give rGO‐g‐PAA/PAM hydrogels. The interfacial interaction between GO/rGO and hydrogel matrix was improved by reversible hydrogen bonds between the grafted PAA chains and PAM matrix. Consequently, both GO‐g‐PAA/PAM and rGO‐g‐PAA/PAM hydrogels exhibited improved tensile properties, excellent energy dissipation, and rapid self‐recovery. The in situ chemical reduction of GO‐g‐PAA in hydrogel matrix endowed rGO‐g‐PAA/PAM hydrogels with satisfactory electrical conductivity and obvious resistance change upon stretching. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48781.

“…Previous studies have shown that a crossbar structure based on memristors can accelerate various artificial neural networks (ANNs) by directly mapping vector-matrix multiplication (VMM), a most intensive computing component, to electrical parameters relying on Ohm's law and Kirchhoff's law. [6,7] Under this principle, the VMM computing process directly happened in situ, thus avoiding the memory wall (von Neumann Bottleneck) caused by fetching data from memory. Especially in supervised learning, it can reduce the computational complexity of feed forward process and backpropagation from NP to P. [8] Therefore, current studies mostly focus on classification and regression tasks to make use of this new computing mechanism as a complement to complementary metal oxide semiconductor (CMOS) circuits.…”

Section: Introductionmentioning

confidence: 99%

Memristor‐Based Biologically Plausible Memory Based on Discrete and Continuous Attractor Networks for Neuromorphic Systems

Advanced Intelligent Systems

Liu

et al. 2020

To approach an advanced neuromorphic system, a significant unsettled problem is how to realize biologically plausible memory structures that are dramatically different from classical computers. Herein, a physical system based on memristors is simulated to realize associative memory based on discrete attractor networks, which is essentially content‐based storage, and the influence of device characteristics on network performance is systematically studied. An in situ unsupervised learning method is applied to make greater use of array structure and competitions between neurons, demonstrating significant performance improvement in memory capacity and noise tolerance compared with existing supervised approaches. By extending to continuous attractor neural networks (CANNs), working memory is realized based on memristors for the first time via simulation, and the write and read noises in memristor arrays are found to have different impacts on the ability of CANN in maintaining dynamic information. This work lays a foundation for the construction of future advanced neuromorphic computing systems.