Self-Modulating Interfacial Cation Migration Induced Threshold Switching in Bilayer Oxide Memristive Device

Yin, Jungang; Zeng, Fei; Wan, Qin; Sun, Yiming; Hu, Yuandong; Liu, Jialu; Li, Guoqi; Pan, Feng

doi:10.1021/acs.jpcc.8b09793

Cited by 16 publications

(16 citation statements)

References 52 publications

(95 reference statements)

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“…2e–g ), the complete h-Hf 6 O CF is enclosed by the surrounding m-HfO 2 shell (region 4). Coincidentally, crystallization of the surrounding oxide after the formation of complete CFs has also been observed in cation-based memristors 31 , 46 . Therefore, we propose that the CFs of HfO 2 -based RS memristors are accompanied by a nonconductive crystallization region, which has been generally ignored in previous studies on the mechanism of RS memristors.…”

Section: Resultsmentioning

confidence: 81%

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

et al. 2021

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The resistive switching effect in memristors typically stems from the formation and rupture of localized conductive filament paths, and HfO2 has been accepted as one of the most promising resistive switching materials. However, the dynamic changes in the resistive switching process, including the composition and structure of conductive filaments, and especially the evolution of conductive filament surroundings, remain controversial in HfO2-based memristors. Here, the conductive filament system in the amorphous HfO2-based memristors with various top electrodes is revealed to be with a quasi-core-shell structure consisting of metallic hexagonal-Hf6O and its crystalline surroundings (monoclinic or tetragonal HfOx). The phase of the HfOx shell varies with the oxygen reservation capability of the top electrode. According to extensive high-resolution transmission electron microscopy observations and ab initio calculations, the phase transition of the conductive filament shell between monoclinic and tetragonal HfO2 is proposed to depend on the comprehensive effects of Joule heat from the conductive filament current and the concentration of oxygen vacancies. The quasi-core-shell conductive filament system with an intrinsic barrier, which prohibits conductive filament oxidation, ensures the extreme scalability of resistive switching memristors. This study renovates the understanding of the conductive filament evolution in HfO2-based memristors and provides potential inspirations to improve oxide memristors for nonvolatile storage-class memory applications.

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Section: Resultsmentioning

confidence: 81%

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

et al. 2021

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“…Two of the most common structures, Ag/SiO 2 /Pt (ASP) and Ag/HfO 2 /Pt (AHP) were fabricated by magnetron sputtering at room temperature without breaking the chamber vacuum. [ 33 ] The two‐terminal ASP and AHP memristors are measured based on a semi‐conductor analyzer probe station. For basic direct current (DC), I–V measurements, an external bias is applied to the Ag top electrodes (TE) and the Pt bottom electrodes (BE) are grounded.…”

Section: Electrodesmentioning

confidence: 99%

Design of a Controllable Redox‐Diffusive Threshold Switching Memristor

Sun

Song

Yin

et al. 2020

Adv Elect Materials

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In most cases, the current flows uniformly through the device in the HRS and is restricted to a local region with high conductance known as a conducting filament (CF) in the LRS. [3] Among them, a specific memory device is termed as conductivebridge RRAM (CBRAM), where the formation/rupture of metallic conductive filaments are dominated by cation migration and redox processes. [4] Meanwhile, volatile resistance switching behaviors are commonly observed in CBRAM as threshold switching (TS). Analogous to the nonvolatile electrochemical metallization mechanism in terms of materials and structures, [5] the electrical resistance of such a threshold switching memristor (TSM) could decrease by orders of magnitude when an electric field is applied due to the formation of CFs with active metal (such as Ag or Cu) atoms. [6] Differently, the resistance returns back spontaneously after the termination of the external bias, yielding a superior I-V nonlinearity and unique temporal conductance evolution dynamics. [7] Recent years, the threshold switching memristors based on active metals are also called "diffusive memristors" to emphasize the diffusion dynamics of the metal species. [8] To be more comprehensively, redox-diffusive threshold switching memristor (RDTSM) might be the best choice to demonstrate the overall switching behavior. [1a] RDTSM has gained significant attention due to its similar advantages to RRAM, such as the simple structure, great fabricability and integrability, and compatibility with conventional CMOS technology. More importantly, it has great potential in many vital applications, such as two-terminal selectors with high nonlinearity, [9] high-powerefficient synaptic or neuronal devices with novel functions. [8,10] A specific application requires a correspondingly proper device. Figure 1 displays the measurement schematic of the key parameters of RDTSM, including selectivity (or nonlinearity), compliance currents, switching voltages (including threshold voltages and hold voltages),TS mode (unidirectional and bidirectional, as shown in Figure 1a,b, respectively), switching slopes (Figure 1c), response time (including delay and relaxation, as shown in Figure 1d) and endurance, which are all up to the material composition and device structure. [11] Therefore, With the rapid development of information technique in the big-data era, there is an extremely urgent demand for new circuit building blocks, represented by resistive switching memristors with high speed, high-density integration, and power-efficiency, to overcome the limitations of electronic device scaling and thus achieve non-von-Neumann neuromorphic computing. Redox-diffusive threshold switching memristors, based on the volatile formation/rupture of metallic conductive filaments, are attracting great attention for many novel applications, ranging from selectors to synaptic and neuronal devices. Here, how to design a proper redox-diffusive threshold switching memristor is comprehensively introduced, with particular focus on the effect of the devic...

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“…[60,61,101,[107][108][109][110] The Schottky diode maintains the OFF state in the cutoff region, therefore, it is only used for unipolar memristive devices. [111] Devices based on multilayer oxide/nitride junctions show the gradually varying I-V curve with high nonlinearity, mitigating the sneak path current. [112] The nonlinearity is also realized with the abruptly varied I-V curve based on MIT or TS.…”

Section: Crossbar Arraymentioning

confidence: 99%

Artificial Neural Networks Based on Memristive Devices: From Device to System

Huang

Wang

et al. 2020

Advanced Intelligent Systems

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Thanks to the advance in transistor scaling, computing capabilities of central processing units (CPUs) and graphics processing units (GPUs) have been increased greatly, resulting in the fast development of artificial neural networks (ANNs). ANNs predict the correlation between a set of input and output parameters by emulating the principles of biological neural networks. [1] Nowadays, ANNs have been applied to a broad spectrum, such as computer vision, natural language processing, autonomous driving, and decision making. Although ANNs are advantageous in some specific tasks, such as playing the game of Go, [2,3] the human brain outperforms ANNs in cognitive and classification tasks with fast speed and high power efficiency. The advantages of the human brain lie in the special architectures with high massive parallelism, spiking neurons, and enormous number of basic computing units of neurons (%10 11) and synapses (%10 15), whereas computers show high speed of basic operation and precision. [4] The massively parallel processing compensates the low speed of neurons and synapses, whereas spiking neurons compute more powerful than other types of neurons. [5] The parallel processing occurs based on variable synaptic weights, which is the basis of memory. Moreover, the same information is processed by many neurons and transmitted to a downstream neuron, which enhances the precision as well. Therefore, the human brain shows a salient feature, i.e., in-memory computing, suitable for high-performance applications with a large amount of data. In contrast, conventional digital computing systems face the Von Neumann bottleneck, because in the Von Neumann architecture, computing units and memory are separated, hindering the enhancement of ANNs. Thus, several novel architectures are promoted, such as brain-inspired computing (processing information based on spiking neurons, also called as spiking neural networks [SNNs]) and in-memory computing on chip, to overcome the Von Neumann bottleneck. [6] To implement the new architectures, artificial synapses and neurons are necessary. The massive parallelism of the brain takes advantage of a large number of neurons and synapses; however, the emulation of the functions of biological synapses and neurons requires tens of complement metal-oxide-semiconductor (CMOS) transistors. Therefore, it is urgent to realize synapses and neurons with a simplified method for the development of ANNs. [7] As synapses adjust their weights step-by-step repeatedly, synaptic devices need to exhibit analog switching behavior. [8] However, biological neurons show the analog membrane potential and generate the all-or-nothing rapidly, so both analog and digital devices are acceptable for the emulation of neurons. Many emerging devices have been proposed to develop ANNs based on hardware, e.g., memristive devices, phase change memories (PCMs), [9] magnetic random-access memories (MRAMs), [10] and ferroelectric field-effect transistors (FeFETs). [11] Each technology has its advantages and disadvantages. PCM and M...

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Self-Modulating Interfacial Cation Migration Induced Threshold Switching in Bilayer Oxide Memristive Device

Cited by 16 publications

References 52 publications

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

Evolution of the conductive filament system in HfO2-based memristors observed by direct atomic-scale imaging

Design of a Controllable Redox‐Diffusive Threshold Switching Memristor

Artificial Neural Networks Based on Memristive Devices: From Device to System

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