Reliability of analog resistive switching memory for neuromorphic computing

Zhao, Meiran; Gao, Bin; Tang, Jianshi; Qian, He; Wu, Huaqiang

doi:10.1063/1.5124915

Cited by 235 publications

(194 citation statements)

References 80 publications

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“…Nonideal factors such as nonlinearity, asymmetry, and limited conductance range have been intensively studied in device and system-level analysis, [70] but reliability concerns such as data retention, cycling endurance, variability, and failure have been less discussed and explored. [71,72] The conductance states can be affected in unexpected ways by various reliability issues. For simplicity, the conductance degradation trends were categorized in two major ways by considering whether the weighted sum current was consistently changed toward a certain direction or not.…”

Section: Rrammentioning

confidence: 99%

Recent Advancements in Emerging Neuromorphic Device Technologies

Woo

Kim

et al. 2020

Advanced Intelligent Systems

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The explosive growth of data and information has motivated technological developments in computing systems that utilize them for efficiently discovering patterns and gaining relevant insights. Inspired by the structure and functions of biological synapses and neurons in the brain, neural network algorithms that can realize highly parallel computations have been implemented on conventional silicon transistor‐based hardware. However, synapses composed of multiple transistors allow only binary information to be stored, and processing such digital states through complicated silicon neuron circuits makes low‐power and low‐latency computing difficult. Therefore, the attractiveness of the emerging memories and switches for synaptic and neuronal elements, respectively, in implementing neuromorphic systems, which are suitable for performing energy‐efficient cognitive functions and recognition, is discussed herein. Based on a literature survey, recent progress concerning memories shows that novel strategies related to materials and device engineering to mitigate challenges are presented to primarily achieve nonvolatile analog synaptic characteristics. Attempts to emulate the role of the neuron in various ways using compact switches and volatile memories are also discussed. It is hoped that this review will help direct future interdisciplinary research on device, circuit, and architecture levels of neuromorphic systems.

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Section: Rrammentioning

confidence: 99%

Recent Advancements in Emerging Neuromorphic Device Technologies

Woo

Kim

et al. 2020

Advanced Intelligent Systems

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“…Recently, resistive switching memory has emerged as a promising contender for next‐generation data storage technology due to its advantages of simple structure and flexibility 5‐12 . Besides, resistive memory holds great potential to implement high storage capacity, fast data transfer rate, short access time, low power consumption, and neuromorphic computing, which can satisfy the critical requirements for upcoming UHDDS electronics and artificial intelligent technologies 12‐14 . A typical resistive memory device generally consists of two electrodes and a switching layer between them, which can switch between high and low resistance (conductance) states in response to an external electric voltage 8,15 .…”

Section: Introductionmentioning

confidence: 99%

Recent advances in organic‐based materials for resistive memory applications

Qian

Zhu

et al. 2020

InfoMat

147

142

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With the rapid development of data‐driven human interaction, advanced data‐storage technologies with lower power consumption, larger storage capacity, faster switching speed, and higher integration density have become the goals of future memory electronics. Nevertheless, the physical limitations of conventional Si‐based binary storage systems lag far behind the ultrahigh‐density requirements of post‐Moore information storage. In this regard, the pursuit of alternatives and/or supplements to the existing storage technology has come to the forefront. Recently, organic‐based resistive memory materials have emerged as promising candidates for next‐generation information storage applications, which provide new possibilities of realizing high‐performance organic electronics. Herein, the memory device structure, switching types, mechanisms, and recent advances in organic resistive memory materials are reviewed. In particular, their potential of fulfilling multilevel storage is summarized. Besides, the present challenges and future prospects confronted by organic resistive memory materials and devices are discussed.

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Section: Introductionmentioning

confidence: 99%

“…Significant effort has been devoted to realizing the functionalities of neurons and synapses for neuromorphic computing using emerging nonvolatile memories (NVMs) [ 9 ]. Recently, several memristive NVMs, including resistive random-access memory and phase-change memory, have emerged [ 9 , 10 , 11 , 12 , 13 ]. Memristive devices, which exhibit history-dependent conductivity modulation [ 4 , 8 , 9 , 10 , 11 , 12 , 13 ], are more efficient than traditional Si-based complementary metal-oxide semiconductor (CMOS) circuits [ 6 ] in providing more capable neuromorphic systems.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several memristive NVMs, including resistive random-access memory and phase-change memory, have emerged [ 9 , 10 , 11 , 12 , 13 ]. Memristive devices, which exhibit history-dependent conductivity modulation [ 4 , 8 , 9 , 10 , 11 , 12 , 13 ], are more efficient than traditional Si-based complementary metal-oxide semiconductor (CMOS) circuits [ 6 ] in providing more capable neuromorphic systems. They can store information at the nanoscale in an analogue way and allow in-memory computing, which is a major approach toward energy-efficient computing paradigms beyond the von-Neumann architecture [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Approach to the Fabrication of Memristive Neuromorphic Devices: Compositionally Graded Films

Yoon

2020

Materials

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Energy-efficient computing paradigms beyond conventional von-Neumann architecture, such as neuromorphic computing, require novel devices that enable information storage at nanoscale in an analogue way and in-memory computing. Memristive devices with long-/short-term synaptic plasticity are expected to provide a more capable neuromorphic system compared to traditional Si-based complementary metal-oxide-semiconductor circuits. Here, compositionally graded oxide films of Al-doped MgxZn1−xO (g-Al:MgZnO) are studied to fabricate a memristive device, in which the composition of the film changes continuously through the film thickness. Compositional grading in the films should give rise to asymmetry of Schottky barrier heights at the film-electrode interfaces. The g-Al:MgZnO films are grown by using aerosol-assisted chemical vapor deposition. The current-voltage (I-V) and capacitance-voltage (C-V) characteristics of the films show self-rectifying memristive behaviors which are dependent on maximum applied voltage and repeated application of electrical pulses. Endurance and retention performance tests of the device show stable bipolar resistance switching (BRS) with a short-term memory effect. The short-term memory effects are ascribed to the thermally activated release of the trapped electrons near/at the g-Al:MgZnO film-electrode interface of the device. The volatile resistive switching can be used as a potential selector device in a crossbar memory array and a short-term synapse in neuromorphic computing.

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Reliability of analog resistive switching memory for neuromorphic computing

Cited by 235 publications

References 80 publications

Recent Advancements in Emerging Neuromorphic Device Technologies

Recent Advancements in Emerging Neuromorphic Device Technologies

Recent advances in organic‐based materials for resistive memory applications

A New Approach to the Fabrication of Memristive Neuromorphic Devices: Compositionally Graded Films

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