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      A Si-memristor electronically and uniformly switched by a constant voltage

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          Abstract

          Amorphous insulators have localized wave functions that decay with the distance \(r\) following exp(\(-r/\zeta\)). Since nanoscale conduction is not excluded at \(r<\zeta\), one may use amorphous insulators and take advantage of their size effect for nanoelectronic applications. Voltage-regulated nanoscale conductivity is already utilized in metal-insulator-metal devices known as memristors. But typically their tunable conductivity does not come from electrons but from migrating ions within a stoichastically formed filament, and as such their combined resistor-memory performance suffers. Here we demonstrate amorphous-silicon-based memristors can have coherent electron wave functions extending to the full device thickness, exceeding 15 nm. Remarkably, despite the large aspect ratio and very thin thickness of the device, its electrons still follow an isotropic, three-dimensional pathway, thus providing uniform conductivity at the nanometer scale. Such pathways in amorphous insulators are derived from overlapping gap states and regulated by trapped charge, which is stabilized by electron-lattice interaction; this makes the memristor exhibit pressure-triggered insulator\(\rightarrow\)metal transitions. Fast, uniform, durable, low-power and purely electronic memristors with none of the shortcomings of ion-migrating memristors have been fabricated from a variety of amorphous silicon compositions and can be readily integrated into silicon technology. Therefore, amorphous silicon may provide the ideal platform for building proximal memories, transistors and beyond.

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          Most cited references18

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          The future of electronics based on memristive systems

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            Nanofilamentary resistive switching in binary oxide system; a review on the present status and outlook.

            This review article summarized the recent understanding of resistance switching (RS) behavior in several binary oxide thin film systems. Among the various RS materials and mechanisms, TiO(2) and NiO thin films in unipolar thermo-chemical switching mode are primarily dealt with. To facilitate the discussions, the RS was divided into three parts; electroforming, set and reset steps. After short discussions on the electrochemistry of 'electrolytic' oxide materials, the general and peculiar aspects of these RS systems and mechanism are elaborated. Although the RS behaviors and characteristics of these materials are primarily dependent on the repeated formation and rupture of the conducting filaments (CFs) at the nanoscale at a localized position, this mechanism appears to offer a basis for the understanding of other RS mechanisms which were originally considered to be irrelevant to the localized events. The electroforming and set switching phenomena were understood as the process of CF formation and rejuvenation, respectively, which are mainly driven by the thermally assisted electromigration and percolation (or even local phase transition) of defects, while the reset process was understood as the process of CF rupture where the thermal energy plays a more crucial role. This review also contains several remarks on the outlook of these resistance change devices as a semiconductor memory.
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              A low energy oxide-based electronic synaptic device for neuromorphic visual systems with tolerance to device variation.

              Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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                Author and article information

                Journal
                11 June 2018
                Article
                1806.04300
                40ce7f76-d262-4368-9bbc-d802c8e8e829

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                cond-mat.mes-hall

                Nanophysics
                Nanophysics

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