An overview of resistive random access memory devices

Li, Yingtao; Long, Shibing; Liu, Qi; Lü, HangBing; Liu, Su; Liu, Ming

doi:10.1007/s11434-011-4671-0

Cited by 87 publications

(52 citation statements)

References 38 publications

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“…A C C E P T E D ACCEPTED MANUSCRIPT materials, such as switching mode, operation speed, endurance, etc, has been well summarized in previous reviews [1][2][3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: A N U S C R I P Tmentioning

confidence: 99%

“…The academic and industrial research topics on RRAM cover quite a wide range: materials problem, RS mechanism, manufacture, integration, and even other function beyond the data storage, as reviewed previously [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In this review article, we concentrate on the materials science issue including the material selection and the corresponding RS mechanisms.…”

Section: Introductionmentioning

confidence: 99%

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An overview of materials issues in resistive random access memory

Zhu

Zhou

Guo

et al. 2015

Journal of Materiomics

118

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Resistive random access memory (RRAM) is a very promising next generation non-volatile RAM, with quite significant advantages over the widely used silicon-based Flash memories. For RRAM, material with switchable resistance, working as the storage medium, is the most important part for the performance of the memory. In this review, as a start, some general hints for the materials selection are proposed. Then most recent studies on this emerging memory from the perspective of materials science are summarized: various materials with resistance switch (RS) behavior and the underlying mechanisms are introduced; as a complementary to the previous review articles, here the increasingly important role of computational materials science in the research of RRAM is presented and highlighted. By incorporating the framework of high-throughput calculation and multi-scale simulations, design process of new RRAM could be accelerated and more cost-effective.

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Section: A N U S C R I P Tmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An overview of materials issues in resistive random access memory

Zhu

Zhou

Guo

et al. 2015

Journal of Materiomics

118

View full text Add to dashboard Cite

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“…In 2008, Strukov and Williams reported a connection to the mathematical memristor concept developed by Chua . In general, a resistive switch can attain different resistance states which are controlled by the polarity or magnitude of an applied bias resulting in pinched hysteretic I–V profiles . In recent years, resistive switching has been reported for various classes of materials ranging from sulfides (e.g., Cu 2 S, Ag 2 S) to binary oxides (e.g., TiO 2 , SiO 2 , CuO, NiO, CoO, Fe 2 O 3 , MoO, VO 2 ) over to complex oxides (e.g., SrTiO 3‐δ , (La , Sr)MnO 3 , (Pr,Ca)MnO 3 , BaTiO 3 , (La,Sr)(Co,Fe)O 3 , CeCu 3 Ti 4 O 12 ) .…”

Section: Introductionmentioning

confidence: 99%

Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO_3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance

Messerschmitt

Kubicek

Schweiger

et al. 2014

Adv Funct Materials

101

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Memristors based on mixed anionic‐electronic conducting oxides are promising devices for future data storage and information technology with applications such as non‐volatile memory or neuromorphic computing. Unlike transistors solely operating on electronic carriers, these memristors rely, in their switch characteristics, on defect kinetics of both oxygen vacancies and electronic carriers through a valence change mechanism. Here, Pt|SrTiO3‐δ|Pt structures are fabricated as a model material in terms of its mixed defects which show stable resistive switching. To date, experimental proof for memristance is characterized in hysteretic current–voltage profiles; however, the mixed anionic‐electronic defect kinetics that can describe the material characteristics in the dynamic resistive switching are still missing. It is shown that chronoamperometry and bias‐dependent resistive measurements are powerful methods to gain complimentary insights into material‐dependent diffusion characteristics of memristors. For example, capacitive, memristive and limiting currents towards the equilibrium state can successfully be separated. The memristor‐based Cottrell analysis is proposed to study diffusion kinetics for mixed conducting memristor materials. It is found that oxygen diffusion coefficients increase up to 3 × 10–15 m2s–1 for applied bias up to 3.8 V for SrTiO3‐δ memristors. These newly accessible diffusion characteristics allow for improving materials and implicate field strength requirements to optimize operation towards enhanced performance metrics for valence change memristors.

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“…Memristive device, or resistance switch, [1][2][3][4][5][6][7] stands out as one of the leading candidates for emerging memory and computing technologies to address challenges in data-centric applications. [8][9][10][11][12][13] Although the memristive switching phenomenon has been observed in various materials, silicon oxide is a promising choice because it is well studied and fully compatible with the complementary metal-oxide-semiconductor technology.…”

Section: Introductionmentioning

confidence: 99%

Scalable 3D Ta:SiO_x Memristive Devices

Jiang

Lin

et al. 2019

Adv Elect Materials

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and hence is more bit cost-efficient. [25] However, 3D vertical structure with SiO x switching layer has not been achieved yet.Herein, we report a Ta:SiO 2 device with reliable bipolar memristive switching. After introducing Ta cations, our devices showed high uniformity, superior endurance (>10 9 cycles), fast switching speed, reliable retention, and analog modulation of device conductance. In addition, we studied the scaling ability of the Ta:SiO 2 -based 3D vertical devices with sizes ranging from micro-to nanoscale (as small as 60 × 15 nm 2 ). The switching behavior shows weak dependence on area size when the device is relatively large; after that, state conductance and the operation current decrease when the device is further scaled down. We built 3D devices and achieved similar memristive behavior from both the top and bottom cells in a two-layer vertical device. Our work confirms that doping SiO x is an efficient way to tailor properties of memristive devices, which has great scalability and stackability. Results and Discussion Ta:SiO 2 Thin Films Prepared by Co-SputteringThe Ta:SiO 2 thins film were prepared by co-sputtering from Ta and SiO 2 targets. Figure 1 schematically shows the principle of the co-sputtering process, in which two targets are sputtered simultaneously in the chamber. The SiO 2 target was sputtered at a constant radiofrequency (RF) power of 270 W, while the Ta target was sputtered with varied direct current (DC) powers ranging from 0 to 20 W. The atomic ratios for Ta dopants sputtered at 10 and 20 W were determined to be 14.1% and 22.5%, respectively. More importantly, the Ta in the thin films is in different valence states, as revealed by X-ray photoemission spectroscopic (XPS) characterization (Figure 2). The Ta 4f XPS spectra from films after 4 min Ar + sputtering inside the chamber were deconvolved into three chemical states of Ta: fully oxidic (Ta 5+ ), suboxidic, and metallic states. [29] The atomic ratios of the three states were calculated to be 57.65%/25.97%/16.39% (Figure 2a) and 34.91%/29.81%/35.28% (Figure 2b), respectively, for the two films prepared with different DC powers on Ta. The concentration of the Ta metallic state increased evidently in the film with a higher Ta sputtering power.From electrical measurements (see Figure S1 in the Supporting Information), the device without Ta doping was hard breakdown, while the device based on Ta 22.5% :SiO 2 was directly shorted. On the contrary, reliable memristive switching A highly reliable memristive device based on tantalum-doped silicon oxide is reported, which exhibits high uniformity, robust endurance (≈1 × 10 9 cycles), fast switching speed, long retention, and analog conductance modulation. Devices with junction areas ranging from microscale to as small as 60 × 15 nm 2 are fabricated and electrically characterized. ON-/OFF-conductance and reset current show weak area dependence when the device is relatively large, and they become proportional to the device area when further scaled down. Two-layer devices with repeatabl...

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An overview of resistive random access memory devices

Cited by 87 publications

References 38 publications

An overview of materials issues in resistive random access memory

An overview of materials issues in resistive random access memory

Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO_3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance

Scalable 3D Ta:SiO_x Memristive Devices

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An overview of resistive random access memory devices

Cited by 87 publications

References 38 publications

An overview of materials issues in resistive random access memory

An overview of materials issues in resistive random access memory

Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance

Scalable 3D Ta:SiOx Memristive Devices

Contact Info

Product

Resources

About

Memristor Kinetics and Diffusion Characteristics for Mixed Anionic‐Electronic SrTiO_3‐δ Bits: The Memristor‐Based Cottrell Analysis Connecting Material to Device Performance

Scalable 3D Ta:SiO_x Memristive Devices