Control of Resistive Switching Voltage by Nanoparticle‐Decorated Wrinkle Interface

Abstract

The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aelm.201800503. Polymer Memory DevicesPolymer memory devices consisting of a memristive electroactive layer sandwiched between two electrodes is a promising candidate for future emerging memory technology because of its simple architecture as well as superior properties of fast access and store speed, high-density with 3D stacks from Figure 4. a) The plot of current as function of applied voltage for the WORM memory device in the positive sweep. Schematic diagrams of the proposed resistance switching mechanism for w-rGO/ Ag NPs/PMMA/Al memory device: b) the initial state (OFF state, region A), and c) the high current state (ON state, region B).

Toward Controlling Filament Size and Location for Resistive Switches via Nanoparticle Exsolution at Oxide Interfaces

“…For example Au nanoparticles were delivered to a Ti-patterned bottom electrode by a porter protein with a Ti binding peptide. [38] In terms of the nanoparticle material, Ag [35,37,39,40] or Au [36,38] have proved to be most popular, though the use of Pt, [23,42] Ru, [29] Cu, [34] or Co [43] has been reported as well. In the case of Ag or Cu nanoparticles, metal ions often migrate into the oxide during operation, actively taking part in the switching mechanism.…”

“…In order to address the challenge of stochastic filament location, several strategies have been put forward to spatially confine filaments either in the switching or electrode materials. These include 1) switching within a single dislocation in SrTiO 3 [9] and in SiGe, [10] 2) fabricating electrodes into tips, [11][12][13][14][15][16][17][18] 3) integrating nanoporous graphene into the switching material, [19][20][21][22] 4) embedding nanoparticles into the switching material, [23][24][25][26][27][28][29][30][31][32][33] 5) introducing nanoparticles at the metal-oxide interface, [34][35][36][37][38][39][40] and 6) engineering the edges of the devices, which has been shown to be industrially viable. [41] These strategies are analyzed in terms of their opportunities, processing challenges and materials universality in Table 1, and summarized through device [35] sketches in Figure 1a.…”

Metal-containing organic compounds for memory and data storage applications

“…Recently, Huang et al proposed a method to adjust the switching voltage of the memory device by sandwiching Ag NPs between wrinkle reduced graphene oxide (w-rGO) and PMMA layer to modify the roughness of rGO. 170 The device based on the resulting smooth-surface rGO (s-rGO) (s-rGO/PMMA/Al) exhibited a WORM-type non-volatile memory behavior with improved performance (ultra-low V th of 0.9 V, high I ON /I OFF ratio of 10 3 , and desirable long retention time over 10 4 s) as compared to the one with w-rGO. (Fig.…”

A Spiking Stochastic Neuron Based on Stacked InGaZnO Memristors

“…[1][2][3] Such networks have been demonstrated with favorable properties such as low power consumption, fast inference, and event-driven information processing. [4][5][6][7] Various neurons models can be applied for an SNN, such as the Hodgkin-Huxley neuron, [8,9] leaky integrate-and-fire neurons, [10][11][12] and oscillation neurons. [13] Among them, stochastic neuron performs well in description of enhanced sensing, training, and recognition of the datasets.…”