Modeling of RRAM With Embedded Tunneling Barrier and Its Application in Logic in Memory

Lee, Jia-Wei; Chiang, Meng-Hsueh

doi:10.1109/jeds.2020.3008172

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…It could quickly form a conductive path that leads to LRS Ohmic current and reduces the tunneling gap distance. The time and the thermal-related positive feedback behavior are expressed as [12] 𝑑𝑔 𝑑𝑡 = 𝑣 0 𝑒𝑥𝑝 (…”

Section: B Gap Formationmentioning

confidence: 99%

Modeling of Bilayer Modulated RRAM and Its Array Performance for Compute-in-Memory Applications

Lee,

Chou,

Chen

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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This paper presents a modified compact model of resistive random-access memory (RRAM) with a tunneling barrier. The bilayer-modulated RRAM can be integrated into a higher-density array, reducing leakage current in standby mode. The model demonstrates current transition behavior from low to high bias regions by considering both bulk-limited and electrodelimited transport mechanisms. This model can evaluate RRAM array performance under various pulsing conditions and device parameter variations with calibrated model cards. The computein-memory application requires precise current sum results hindered by the wire resistance loading effect. This study also evaluates various sizes of arrays suitable for performance improvement.

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Section: B Gap Formationmentioning

confidence: 99%

Modeling of Bilayer Modulated RRAM and Its Array Performance for Compute-in-Memory Applications

Lee,

Chou,

Chen

et al. 2023

IEEE J. Explor. Solid-State Comput. Devices Circuits

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Attojoule Hexagonal Boron Nitride‐Based Memristor for High‐Performance Neuromorphic Computing

Kim,

Song,

Kwak

et al. 2024

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In next‐generation neuromorphic computing applications, the primary challenge lies in achieving energy‐efficient and reliable memristors while minimizing their energy consumption to a level comparable to that of biological synapses. In this work, hexagonal boron nitride (h‐BN)‐based metal‐insulator‐semiconductor (MIS) memristors operating is presented at the attojoule‐level tailored for high‐performance artificial neural networks. The memristors benefit from a wafer‐scale uniform h‐BN resistive switching medium grown directly on a highly doped Si wafer using metal–organic chemical vapor deposition (MOCVD), resulting in outstanding reliability and low variability. Notably, the h‐BN‐based memristors exhibit exceptionally low energy consumption of attojoule levels, coupled with fast switching speed. The switching mechanisms are systematically substantiated by electrical and nano‐structural analysis, confirming that the h‐BN layer facilitates the resistive switching with extremely low high resistance states (HRS) and the native SiOx on Si contributes to suppressing excessive current, enabling attojoule‐level energy consumption. Furthermore, the formation of atomic‐scale conductive filaments leads to remarkably fast response times within the nanosecond range, and allows for the attainment of multi‐resistance states, making these memristors well‐suited for next‐generation neuromorphic applications. The h‐BN‐based MIS memristors hold the potential to revolutionize energy consumption limitations in neuromorphic devices, bridging the gap between artificial and biological synapses.

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Modeling of RRAM With Embedded Tunneling Barrier and Its Application in Logic in Memory

Cited by 2 publications

References 25 publications

Modeling of Bilayer Modulated RRAM and Its Array Performance for Compute-in-Memory Applications

Modeling of Bilayer Modulated RRAM and Its Array Performance for Compute-in-Memory Applications

Attojoule Hexagonal Boron Nitride‐Based Memristor for High‐Performance Neuromorphic Computing

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