From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

Moon Seok Kim; Sungho Kim

doi:10.1021/acsaelm.4c00428

From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

Moon Seok Kim, Sungho Kim

Semiconductor Engineering

Research output: Contribution to journal › Review article › peer-review

Abstract

This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.

Original language	English
Pages (from-to)	3998-4015
Number of pages	18
Journal	ACS Applied Electronic Materials
Volume	6
Issue number	6
DOIs	https://doi.org/10.1021/acsaelm.4c00428
State	Published - 25 Jun 2024

Bibliographical note

Publisher Copyright:
© 2024 American Chemical Society.

Keywords

2D-material-based memristors
Analog switching
Memristors
Neuromorphic computing
Oxide-based memristors

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acsaelm.4c00428

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title = "From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing",

abstract = "This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.",

keywords = "2D-material-based memristors, Analog switching, Memristors, Neuromorphic computing, Oxide-based memristors",

author = "Kim, {Moon Seok} and Sungho Kim",

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AU - Kim, Sungho

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N2 - This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.

AB - This review presents a comparative analysis of the analog switching performance of oxide- and two-dimensional (2D)-material-based memristors, focusing on their application in neuromorphic computing systems. This study examines various performance metrics such as endurance, energy consumption, and switching characteristics to elucidate how these parameters are influenced by the unique characteristics of the respective materials. By examining both oxide- and 2D-material-based memristors in array configurations, this review provides insights into their suitability for neuromorphic computing, highlighting advancements, challenges, and future research directions in memristor technology.

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JF - ACS Applied Electronic Materials

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ER -

From Oxides to 2D Materials: Advancing Memristor Technologies for Energy-Efficient Neuromorphic Computing

Abstract

Bibliographical note

Keywords

UN SDGs

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