Pt/Al2O3/TaO
 X
 /Ta Self-Rectifying Memristor With Record-Low Operation Current (&lt;2 pA), Low Power (fJ), and High Scalability

Ren, Shengguang; Ni, Run; Huang, Xiaodi; Li, Yi; Xue, Kan‐Hao; Miao, Xiangshui

doi:10.1109/ted.2021.3134137

Cited by 20 publications

(19 citation statements)

References 28 publications

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“…2C. Commonly, the SRMs exhibit forming-free characteristics (27)(28)(29)(30)(31)(32)(33)(34)(35) similar to the proposed SRM cell, as shown in Fig. 2D.…”

Section: Characteristics Of the Srm Arraymentioning

confidence: 53%

“…Owing to its self-rectifying nature, the SRM can substantially outperform other nonvolatile memories, such as the phase-change memory (PCM) and resistive random-access memory (ReRAM), for high-density integration without additional selective devices [4F 2 footprint and prominent three-dimensional (3D) scalability]. Moreover, with their ultralow operating current (down to picoampere level), SRM shows great potential to construct energy-efficient mIMC systems for data-intensive tasks (27)(28)(29)(30)(31)(32)(33). Our SRM cells were fabricated with a vertical stack structure of Pt/HfO 2 /TaO x /Ta (Fig.…”

Section: Characteristics Of the Srm Arraymentioning

confidence: 99%

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Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Ren

et al. 2023

Sci. Adv.

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Memristor-enabled in-memory computing provides an unconventional computing paradigm to surpass the energy efficiency of von Neumann computers. Owing to the limitation of the computing mechanism, while the crossbar structure is desirable for dense computation, the system’s energy and area efficiency degrade substantially in performing sparse computation tasks, such as scientific computing. In this work, we report a high-efficiency in-memory sparse computing system based on a self-rectifying memristor array. This system originates from an analog computing mechanism that is motivated by the device’s self-rectifying nature, which can achieve an overall performance of ~97 to ~11 TOPS/W for 2- to 8-bit sparse computation when processing practical scientific computing tasks. Compared to previous in-memory computing system, this work provides over 85 times improvement in energy efficiency with an approximately 340 times reduction in hardware overhead. This work can pave the road toward a highly efficient in-memory computing platform for high-performance computing.

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“…2C. Commonly, the SRMs exhibit forming-free characteristics (27)(28)(29)(30)(31)(32)(33)(34)(35) similar to the proposed SRM cell, as shown in Fig. 2D.…”

Section: Characteristics Of the Srm Arraymentioning

confidence: 53%

Section: Characteristics Of the Srm Arraymentioning

confidence: 99%

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Ren

et al. 2023

Sci. Adv.

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“…Besides, the scalability of the SRM array is typically evaluated by adopting a one-bitline pull-up approach to simplify the circuit model of the crossbar array in the V read /2 operation mode. [40,81,82,92,95,99,106,107] Figure 3a shows the circuit schematic illustration of an N × N (N ≥ 2) crossbar array, and its equivalent simplified circuit is displayed in Figure 3b. The maximum array size (i.e., scalability) on the premise of a 10% read margin in the worst case scenario is obtained by simulation with the corresponding formulas.…”

Section: State-of-the-art Srmsmentioning

confidence: 99%

Self‐Rectifying Memristors for Three‐Dimensional In‐Memory Computing

Ren,

Dong,

Yang

et al. 2023

Advanced Materials

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Costly data movement in terms of time and energy in traditional von Neumann systems is exacerbated by emerging information technologies related to artificial intelligence (AI). In‐memory computing (IMC) architecture aims to address this problem. Although the IMC hardware prototype represented by a memristor has developed rapidly and performs well, the sneak path issue is a critical and unavoidable challenge prevalent in large‐scale and high‐density crossbar arrays, particularly in three‐dimensional (3D) integration. As a perfect solution to the sneak‐path issue, self‐rectifying memristor (SRM) has been proposed for 3D integration because of its superior integration density. To date, SRMs have performed well in terms of power consumption (∼aJ) and scalability (> 102 Mbit). Moreover, SRM‐configured 3D integration is considered an ideal hardware platform for 3D IMC. This review focuses on the progress in SRMs and their applications in 3D memory, IMC, neuromorphic computing, and hardware security. The advantages, disadvantages, and optimization strategies of SRMs in diverse application scenarios are illustrated. Challenges posed by physical mechanisms, fabrication processes, and peripheral circuits, as well as potential solutions at the device and system levels, are also discussed.This article is protected by copyright. All rights reserved

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“…Year 2019 [11] 2020 [45]2021 [46] 2021 [47] 2020 [48] 2013 [49] 2021 [50] 2021 [51] accumulation that leads to the drawbacks of large variation, long latency, and limited lifetime. Other nonlinear devices, like self-rectifying memristors, are candidates with 3D integration potential [25,51,52]. However, a dense oxide tunneling or barrier layer introduced to provide reverse rectification usually leads to a significant increase in the operating voltage (exceeding 5 V or even 10 V), and thus unfavorable power consumption.…”

Section: Special Typementioning

confidence: 99%

2022 roadmap on neuromorphic devices and applications research in China

Wan¹,

Wan²,

Wu³

et al. 2022

Neuromorph. Comput. Eng.

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The data throughput in the von Neumann architecture based computing system is limited by its separated processing and memory structure and the mismatching speed between the two units. As a result, it’s quite difficult to improve the energy efficiency in conventional computing system, especially for dealing with unstructured data. Meanwhile, artificial intelligence and robotics nowadays still behave poorly in autonomy, creativity and sociality, which has been considered as the unimaginable computational requirement for sensorimotor skills. These two plights have urged the imitation and replication of the biological systems in terms of computing, sensing, and even motoring. Hence, the so-called neuromorphic system has drawn world-wide attention in recent decade, which is aimed at addressing the aforementioned needs from the mimicking of neural system. The recent development on emerging memory devices, nanotechnologies, and materials science have provided an unprecedented opportunity for this aim. This roadmap profiles the potential trend in building neuromorphic systems from the view of Chinese scientists. The content of this roadmap will cover some core topics from multidisciplinary researchers including electronics, computer science, materials, physics, and so on. The perspectives and challenges are also discussed in partly, which may serve as the guidance for the pursuing of high energy-efficient and/or bio-realistic systems that can compute, sense, and act as our human beings. This will also give birth to some excited paradigm breakers and advanced technologies in a wide spectrum of areas.

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Pt/Al₂O₃/TaO_X/Ta Self-Rectifying Memristor With Record-Low Operation Current (<2 pA), Low Power (fJ), and High Scalability

Cited by 20 publications

References 28 publications

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Self‐Rectifying Memristors for Three‐Dimensional In‐Memory Computing

2022 roadmap on neuromorphic devices and applications research in China

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Pt/Al2O3/TaO X /Ta Self-Rectifying Memristor With Record-Low Operation Current (<2 pA), Low Power (fJ), and High Scalability

Cited by 20 publications

References 28 publications

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Sparse matrix multiplication in a record-low power self-rectifying memristor array for scientific computing

Self‐Rectifying Memristors for Three‐Dimensional In‐Memory Computing

2022 roadmap on neuromorphic devices and applications research in China

Contact Info

Product

Resources

About

Pt/Al₂O₃/TaO_X/Ta Self-Rectifying Memristor With Record-Low Operation Current (<2 pA), Low Power (fJ), and High Scalability