Switching characteristics of TaO x -based one diode-one resistor for crossbar memory application

Jin, Yunyun; Xu, Zhenbang; Yoon, Soon Fatt; Chia, C. K.; Wang, Xinbo; Z., D.

doi:10.1007/s13391-016-5369-x

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…[125][126][127][128] Oxide-based diodes have no CMOS compatibility issue. They can also be fabricated with relatively low-temperature processes; [114,123,124,[129][130][131][132][133] for example, Yoon et al reported a 1D1R crossbar array shown in Figure 5a using physical vapor deposition methods at low temperature. [134] The top-view and cross-sectional scanning electron microscopy (SEM) images are shown in Figure 5b, showing the device structure consisting of Ti/TiO 2 /Pt/SiO x /Pt.…”

Section: D1r Cell and Crossbar Arraymentioning

confidence: 99%

Memristive Crossbar Arrays for Storage and Computing Applications

Wang

Zhang

et al. 2021

Advanced Intelligent Systems

116

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The emergence of memristors with potential applications in data storage and artificial intelligence has attracted wide attentions. Memristors are assembled in crossbar arrays with data bits encoded by the resistance of individual cells. Despite the proposed high density and excellent scalability, the sneak‐path current causing cross interference impedes their practical applications. Therefore, developing novel architectures to mitigate sneak‐path current and improve efficiency, reliability, and stability may benefit next‐generation storage‐class memory (SCM). Moreover, conventional digital computers face the von‐Neumann bottleneck and the slowdown of transistors’ scaling, imposing a big challenge to hardware artificial intelligence. Memristive crossbar features colocation of memory and processing units, as well as superior scalability, making it a promising candidate for hardware accelerating machine learning and neuromorphic computing. Herein, first, crossbar architecture is introduced. Then, for storage, the origin of sneak‐path current is reviewed and techniques to mitigate this issue from the angle of materials and circuits are discussed. Computing wise, the applications of memristive crossbars in both machine learning and neuromorphic computing are surveyed, focusing on the structure of unit cells, the network topology, and the learning types. Finally, a perspective on future engineering and applications of memristive crossbars is discussed.

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Section: D1r Cell and Crossbar Arraymentioning

confidence: 99%

Memristive Crossbar Arrays for Storage and Computing Applications

Wang

Zhang

et al. 2021

Advanced Intelligent Systems

116

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“…The sneaking current path is a fundamental problem commonly existing in 2D or 3D cross-bar array, [25][26][27][28][29][30] as shown in Fig. 1.…”

Section: Sneaking Current Problemmentioning

confidence: 99%

Resistive switching memory for high density storage and computing*

Luo

Gong

et al. 2021

Chinese Phys. B

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The resistive random access memory (RRAM) has stimulated a variety of promising applications including programmable analog circuit, massive data storage, neuromorphic computing, etc. These new emerging applications have huge demands on high integration density and low power consumption. The cross-point configuration or passive array, which offers the smallest footprint of cell size and feasible capability of multi-layer stacking, has received broad attention from the research community. In such array, correct operation of reading and writing on a cell relies on effective elimination of the sneaking current coming from the neighboring cells. This target requires nonlinear I–V characteristics of the memory cell, which can be realized by either adding separate selector or developing implicit build-in nonlinear cells. The performance of a passive array largely depends on the cell nonlinearity, reliability, on/off ratio, line resistance, thermal coupling, etc. This article provides a comprehensive review on the progress achieved concerning 3D RRAM integration. First, the authors start with a brief overview of the associative problems in passive array and the category of 3D architectures. Next, the state of the arts on the development of various selector devices and self-selective cells are presented. Key parameters that influence the device nonlinearity and current density are outlined according to the corresponding working principles. Then, the reliability issues in 3D array are summarized in terms of uniformity, endurance, retention, and disturbance. Subsequently, scaling issue and thermal crosstalk in 3D memory array are thoroughly discussed, and applications of 3D RRAM beyond storage, such as neuromorphic computing and CMOL circuit are discussed later. Summary and outlooks are given in the final.

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“…[4] In addition, this leakage becomes severe when all adjacent cells have low resistance values and the crossbar array is scaled up. Various approaches have been conducted to alleviate these interferences through transistors, [5,6] unidirectional diodes, [7][8][9] and bidirectional diodes. [10][11][12][13] However, transistors are active components based on a three-electrode structure, occupying a large footprint of 6F 2 and requiring complicated steps to be downscale to 4F 2 .…”

Section: Introductionmentioning

confidence: 99%

Controlling Threshold and Resistive Switch Functionalities in Ag‐Incorporated Organometallic Halide Perovskites for Memristive Crossbar Array

Kim

Baek

et al. 2022

Adv Funct Materials

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Halide perovskites (HPs) can be the effective functional materials for the sneak-path current issue in the memristive crossbar array. Herein, an efficient strategy is proposed to integrate the HPs-based bidirectional threshold and bipolar resistive switches (TS and RS). The resistance change characteristics from volatile threshold to nonvolatile resistive switching are modulated by controlling Ag doping concentration in the MAPbI 3 . HPs provide the diffusive condition and the quantity of Ag regulates the radius of its network. A low amount of Ag contributes to weak network with a short lifetime. However, when the amount of Ag increases, the conductive filament becomes more robust, showing a long lifetime. A MAPbI 3 :Ag TS with a low Ag content is developed, showing a steep switching slope (1 mV per decade), fast switching speed (< 80 ns), and low off-current (10 nA). And, a MAPbI 3 :Ag RS with a high Ag content is developed, showing multilevel storage capability and long retention time (1400 s). Finally, these TS and RS coupled into the 1S-1R integrated component, resulting the development of the maximum crossbar array size to 1.4 × 10 12 . This study offers an efficient methodology for tailoring the resistance change characteristics and a promising strategy for practical HPs-based memristive crossbar application.

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Switching characteristics of TaO x -based one diode-one resistor for crossbar memory application

Cited by 4 publications

References 14 publications

Memristive Crossbar Arrays for Storage and Computing Applications

Memristive Crossbar Arrays for Storage and Computing Applications

Resistive switching memory for high density storage and computing*

Controlling Threshold and Resistive Switch Functionalities in Ag‐Incorporated Organometallic Halide Perovskites for Memristive Crossbar Array

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