Metal oxide resistive memory with a deterministic conduction path

Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for data-centric applications such as neuromorphic computing. However, RRAMs still suffer from instability caused by uncontrolled filament growth and...

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Published inJournal of materials chemistry. C, Materials for optical and electronic devices Vol. 8; no. 11; pp. 3897 - 393
Main Authors Lee, Sunghwan, Seo, Shem, Lim, Jinho, Jeon, Dasom, Alimkhanuly, Batyrbek, Kadyrov, Arman, Lee, Seunghyun
Format Journal Article
LanguageEnglish
Published Cambridge Royal Society of Chemistry 21.03.2020
Subjects
Antimony
Hafnium oxide
Mathematical analysis
Matrix algebra
Matrix methods
Metal oxides
Multiplication
Random access memory
Switching
Tellurium
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Abstract Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for data-centric applications such as neuromorphic computing. However, RRAMs still suffer from instability caused by uncontrolled filament growth and random oxygen vacancy distribution. In this study, a Ge-Sb-Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO 2 -based RRAM layer to confine the subsequent filament formation to the initially determined site. Based on the DC and pulse measurement data, this technique is confirmed to improve the memory switching reproducibility without compromising its endurance and retention. Such deterministic behavior will be important in improving the sensing margin and multi-level capability of RRAM technology as the switching characteristics become more unstable with extreme device scaling. In this study, a Ge-Sb-Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO 2 -based RRAM layer to improve the memory switching reproducibility and reduce HRS/LRS variations.
AbstractList Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for data-centric applications such as neuromorphic computing. However, RRAMs still suffer from instability caused by uncontrolled filament growth and random oxygen vacancy distribution. In this study, a Ge-Sb-Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO 2 -based RRAM layer to confine the subsequent filament formation to the initially determined site. Based on the DC and pulse measurement data, this technique is confirmed to improve the memory switching reproducibility without compromising its endurance and retention. Such deterministic behavior will be important in improving the sensing margin and multi-level capability of RRAM technology as the switching characteristics become more unstable with extreme device scaling. In this study, a Ge-Sb-Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO 2 -based RRAM layer to improve the memory switching reproducibility and reduce HRS/LRS variations.
Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for data-centric applications such as neuromorphic computing. However, RRAMs still suffer from instability caused by uncontrolled filament growth and random oxygen vacancy distribution. In this study, a Ge–Sb–Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO2-based RRAM layer to confine the subsequent filament formation to the initially determined site. Based on the DC and pulse measurement data, this technique is confirmed to improve the memory switching reproducibility without compromising its endurance and retention. Such deterministic behavior will be important in improving the sensing margin and multi-level capability of RRAM technology as the switching characteristics become more unstable with extreme device scaling.
Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for data-centric applications such as neuromorphic computing. However, RRAMs still suffer from instability caused by uncontrolled filament growth and random oxygen vacancy distribution. In this study, a Ge–Sb–Te ternary chalcogenide layer that functions as a conductive lead is added to a HfO 2 -based RRAM layer to confine the subsequent filament formation to the initially determined site. Based on the DC and pulse measurement data, this technique is confirmed to improve the memory switching reproducibility without compromising its endurance and retention. Such deterministic behavior will be important in improving the sensing margin and multi-level capability of RRAM technology as the switching characteristics become more unstable with extreme device scaling.
Author Lim, Jinho
Alimkhanuly, Batyrbek
Lee, Sunghwan
Seo, Shem
Kadyrov, Arman
Jeon, Dasom
Lee, Seunghyun
AuthorAffiliation Department of Electronic Engineering
Kyunghee University
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Snippet Resistive random access memories (RRAMs) with minimal power dissipation, high speed, and matrix-vector multiplication capability are potentially ideal for...
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SubjectTerms Antimony
Hafnium oxide
Mathematical analysis
Matrix algebra
Matrix methods
Metal oxides
Multiplication
Random access memory
Switching
Tellurium
Title Metal oxide resistive memory with a deterministic conduction path
URI https://www.proquest.com/docview/2378852536
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