8-Layers 3D vertical RRAM with excellent scalability towards storage class memory applications

Luo, Qing; Xu, Xiaoxin; Gong, Tiancheng; Lv, Hangbing; Dong, Danian; Ma, Haili; Yuan, Peng; Gao, Jianfeng; Liu, Jing; Yu, Zhibin; Li, Junfeng; Long, Shibing; Liu, Qi; Liu, Ming

doi:10.1109/iedm.2017.8268315

Cited by 87 publications

(52 citation statements)

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“…The layer-independence of μ_Log(LRS) and σ_Log(LRS) agrees well with results in [6]. At LRS, the current is dominated by Poole-Frenkel emission which gives rise to the relation in (1) [6] ln…”

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confidence: 81%

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Layer‐dependent resistance variability assessment on 2048 8‐layer 3D vertical RRAMs

Gao

Lei

et al. 2019

Electron. lett.

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Non-filament 3D vertical RRAM (VRRAM) is a promising technology for emerging high-density memory applications. In this Letter, the layer-dependent resistance variability at both low resistance state (LRS) and high resistance state (HRS) is assessed on state-of-the-art 8-layer 3D VRRAMs. 2048 devices are measured with the aid of an FPGA-controlled relay matrix which enables automated switching of devices without changing the cabling. The results show LRS exhibits little layer dependence while both the average and the standard variation of HRS decrease from the top layer to the bottom layer. A qualitative speculation is proposed to explain the observation based on the high-resolution transmission electronic microscope image. This work is beneficial for future 3D VRRAM circuit design and performance improvement.

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confidence: 81%

“…Devices and experiments: Fig. 1a depicts the architecture of the 3D VRRAM crossbar used in this work, with the detailed fabrication flow in [6]. A single crossbar consists of 32*8*8 = 2048 devices.…”

mentioning

confidence: 99%

Layer‐dependent resistance variability assessment on 2048 8‐layer 3D vertical RRAMs

Gao

Lei

et al. 2019

Electron. lett.

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“…[78,147] Among these candidates for artificial synapses, RRAM devices appear to be the most attractive option because of the low energy consumption down to sub-pJ per synaptic event, the extreme scalability as crossbar RRAM can have an area of down to 2 × 2 nm 2 , the potential for high-density integration with selector and 3D stacking, [148,149] as well as excellent CMOS fabrication compatibility. Different levels of stable conductance can be obtained through carefully adjusting the pulse amplitude or width to realize gradual SET programing.…”

Section: Memory Switching In Artificial Synapsesmentioning

confidence: 99%

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

et al. 2019

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As the research on artificial intelligence booms, there is broad interest in brain‐inspired computing using novel neuromorphic devices. The potential of various emerging materials and devices for neuromorphic computing has attracted extensive research efforts, leading to a large number of publications. Going forward, in order to better emulate the brain's functions, its relevant fundamentals, working mechanisms, and resultant behaviors need to be re‐visited, better understood, and connected to electronics. A systematic overview of biological and artificial neural systems is given, along with their related critical mechanisms. Recent progress in neuromorphic devices is reviewed and, more importantly, the existing challenges are highlighted to hopefully shed light on future research directions.

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“…Variability can happen from cycle-to-cycle (intra-device) and from device-to-device (inter-device). The variability of the resistance states R ON and R OF F across a matrix is largely influenced by the choice of the stack's materials (e.g., single material HfO X vs. bilayer HfO X +TaO X ) [91], [92], as well as the device scaling. Extreme scaling seems to reduce the variability, probably because of a reduction of the area where the switching occurs [93].…”

Section: Device-to-device and Programming Variabilitymentioning

confidence: 99%

Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

et al. 2020

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We investigate the use and performance of the quasi-static memdiode model (QMM) when incorporated into large cross-point arrays intended for pattern classification tasks. Following Chua's memristive devices theory, the QMM comprises two equations, one equation for the electron transport based on the doublediode circuit with single series resistance and a second equation for the internal memory state of the device based on the so-called logistic hysteron or memory map. Ex-situ trained memdiodes with different MNISTlike databases are used to establish the synaptic weights linking the top and bottom wire networks. The role played by the memdiode electrical parameters, wire resistance and capacitance values, image pixelation, connection schemes, signal-to-noise ratio and device-to-device variability in the classification effectiveness are investigated. The confusion matrix is used to benchmark the system performance metrics. We show that the simplicity, accuracy and robustness of the memdiode model makes it a suitable candidate for the realistic simulation of large-scale neural networks with non-idealities.

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8-Layers 3D vertical RRAM with excellent scalability towards storage class memory applications

Cited by 87 publications

References 1 publication

Layer‐dependent resistance variability assessment on 2048 8‐layer 3D vertical RRAMs

Layer‐dependent resistance variability assessment on 2048 8‐layer 3D vertical RRAMs

Bridging Biological and Artificial Neural Networks with Emerging Neuromorphic Devices: Fundamentals, Progress, and Challenges

Application of the Quasi-Static Memdiode Model in Cross-Point Arrays for Large Dataset Pattern Recognition

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