Circuit-level simulation of resistive-switching random-access memory cross-point array based on a highly reliable compact model

Kim, Min-Hwi; Kim, Sungjun; Ryoo, Kyung−Chang; Cho, Seongjae; Park, Byung‐Gook

doi:10.1007/s10825-017-1116-2

Cited by 5 publications

(4 citation statements)

References 24 publications

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“…2(a)). The switching voltage may vary depending on the switching layer material, the thickness, and the combination with the electrode materials [19,20]. Some devices exhibit gradual set or reset switching over a specific voltage range, and the characteristics can be used to obtain multilevel resistance states using pulse width/amplitude modulation to store multibit data.…”

Section: Spice Compact Model For Multilevel Rram a Rram Cell Characte...mentioning

confidence: 99%

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A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

et al. 2022

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Section: Spice Compact Model For Multilevel Rram a Rram Cell Characte...mentioning

confidence: 99%

“…Several studies have been conducted and reported to realize the general characteristics of RRAM cells [28]. A SPICE compact model was also proposed, and it has been successfully applied to various RRAM devices in a cross-point array [19]. Fig.…”

Section: B Rram Cell Modeling For Spice Simulationmentioning

confidence: 99%

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

et al. 2022

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“…To address these, we optimized the wire and device resistance to guarantee the voltage drops within the acceptable range and used the algorithm that mitigates device variability in ECRAM devices ( 47 ). The programmability of the cross-point devices is enhanced by their three-terminal design, which supports stable, nondestructive read operations and is further improved by recent developments, including the advanced half-bias scheme ( 52 ).…”

Section: Discussionmentioning

confidence: 99%

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator

Noh,

Kwak,

Son

et al. 2024

Sci. Adv.

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We present the fabrication of 4 K-scale electrochemical random-access memory (ECRAM) cross-point arrays for analog neural network training accelerator and an electrical characteristic of an 8 × 8 ECRAM array with a 100% yield, showing excellent switching characteristics, low cycle-to-cycle, and device-to-device variations. Leveraging the advances of the ECRAM array, we showcase its efficacy in neural network training using the Tiki-Taka version 2 algorithm (TTv2) tailored for non-ideal analog memory devices. Through an experimental study using ECRAM devices, we investigate the influence of retention characteristics on the training performance of TTv2, revealing that the relative location of the retention convergence point critically determines the available weight range and, consequently, affects the training accuracy. We propose a retention-aware zero-shifting technique designed to optimize neural network training performance, particularly in scenarios involving cross-point devices with limited retention times. This technique ensures robust and efficient analog neural network training despite the practical constraints posed by analog cross-point devices.

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“…Transition metal oxide materials such as WO3 11,12 , Pr1-xCaxMnO3 13 , TiO2 14 , and MoO3 15,16 have been considered as channel layers for O-ECRAM. Recently, various reports have highlighted ECRAM-based cross-point array architecture 17 including in-situ training 18,19 , demonstrating excellent switching characteristics 20 .…”

Section: Introductionmentioning

confidence: 99%

Unveiling ECRAM Switching Mechanism Using Variable Temperature Hall Measurements for Accelerated AI Computation

Kim,

Kwak,

Choi

et al. 2024

Preprint

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Electrochemical random-access memory (ECRAM) devices are promising cross-point elements for implementing analog cross-point array-based AI computation accelerators due to their remarkable programmability and high-stability driven by ion movement. However, efforts to investigate the fundamental physical parameters governing switching have been limited by the complexity of ECRAM multilayer film structure and the low mobility and high-resistivity of the oxide material. Additionally, tracking the movement of oxygen vacancies within metal oxides has been challenging compared to other active ions. Herein, we fabricate ECRAM devices with multi-terminal Hall-bar structures and conduct AC magnetic parallel dipole line (PDL) Hall measurements. Using two rotating DC magnets and lock-in technique, we extract key transport parameters such as mobility and carrier density of high-resistivity WO3-x channel layer. Through variable-temperature Hall measurements at 50 - 300 K, we experimentally measure, for the first time, the oxygen donor level at approximately 0.1 eV in WO3-x thin film. In addition, we characterize the resistive switching of ECRAM at low temperatures, for the first time, and reveal that the conductance potentiation is caused by increase in both mobility and carrier density. This phenomenon can be attributed to the reversible changes in electronic and atomic structure supported by density functional theory calculation result. Overall, our experimental study on ECRAM switching mechanism contributes to a deep understanding of the operational principles of ECRAM and provides valuable insights for enabling high-performance and energy-efficient AI computation in analog hardware.

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Circuit-level simulation of resistive-switching random-access memory cross-point array based on a highly reliable compact model

Cited by 5 publications

References 24 publications

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

A Fast Weight Transfer Method for Real-Time Online Learning in RRAM-Based Neuromorphic System

Retention-aware zero-shifting technique for Tiki-Taka algorithm-based analog deep learning accelerator

Unveiling ECRAM Switching Mechanism Using Variable Temperature Hall Measurements for Accelerated AI Computation

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