Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

Minjae Kim; Malik Abdul Rehman; Donghyun Lee; Yue Wang; Dong Hyeok Lim; Muhammad Farooq Khan; Haryeong Choi; Qing Yi Shao; Joonki Suh; Hong Sub Lee; Hyung Ho Park

doi:10.1021/acsami.2c12296

Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

Minjae Kim, Malik Abdul Rehman, Donghyun Lee, Yue Wang, Dong Hyeok Lim, Muhammad Farooq Khan, Haryeong Choi, Qing Yi Shao, Joonki Suh, Hong Sub Lee, Hyung Ho Park

Department of Materials Science and Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

Abstract

To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-To-cycle and device-To-device variations. This study experimentally demonstrated and compared the filamentary and interface-Type resistive switching (RS) behaviors of tantalum oxide (Ta₂O₅and TaO₂)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta₂O₅is a strong candidate for memristor, the filament-Type RS behavior of Ta₂O₅does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-Type TaO₂memristor and compared it to a filament type of Ta₂O₅memristor to secure the weight update characteristics and reliability. The TaO₂-based interface-Type memristor exhibited gradual RS characteristics and area dependency in both high-and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO₂-based interface-Type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-Term potentiation (LTP) and long-Term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

Original language	English
Pages (from-to)	44561-44571
Number of pages	11
Journal	ACS Applied Materials and Interfaces
Volume	14
Issue number	39
DOIs	https://doi.org/10.1021/acsami.2c12296
Publication status	Published - 2022 Oct 5

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (grant 2019R1A2C2087604) and Creative Materials Discovery Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (grant 2018M3D1A1058536). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1C1C1006337). Experiments at PLS were partly supported by MEST and POSTECH.

Publisher Copyright:
© 2022 American Chemical Society. All rights reserved.

All Science Journal Classification (ASJC) codes

Materials Science(all)

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10.1021/acsami.2c12296

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@article{23b1ce2ae13f416fb46e0c35880146c8,

title = "Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware",

abstract = "To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-To-cycle and device-To-device variations. This study experimentally demonstrated and compared the filamentary and interface-Type resistive switching (RS) behaviors of tantalum oxide (Ta2O5and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5is a strong candidate for memristor, the filament-Type RS behavior of Ta2O5does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-Type TaO2memristor and compared it to a filament type of Ta2O5memristor to secure the weight update characteristics and reliability. The TaO2-based interface-Type memristor exhibited gradual RS characteristics and area dependency in both high-and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-Type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-Term potentiation (LTP) and long-Term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.",

author = "Minjae Kim and Rehman, {Malik Abdul} and Donghyun Lee and Yue Wang and Lim, {Dong Hyeok} and Khan, {Muhammad Farooq} and Haryeong Choi and Shao, {Qing Yi} and Joonki Suh and Lee, {Hong Sub} and Park, {Hyung Ho}",

note = "Funding Information: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (grant 2019R1A2C2087604) and Creative Materials Discovery Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (grant 2018M3D1A1058536). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1C1C1006337). Experiments at PLS were partly supported by MEST and POSTECH. Publisher Copyright: {\textcopyright} 2022 American Chemical Society. All rights reserved.",

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AU - Kim, Minjae

AU - Rehman, Malik Abdul

AU - Lee, Donghyun

AU - Wang, Yue

AU - Lim, Dong Hyeok

AU - Khan, Muhammad Farooq

AU - Choi, Haryeong

AU - Shao, Qing Yi

AU - Suh, Joonki

AU - Lee, Hong Sub

AU - Park, Hyung Ho

N1 - Funding Information: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (grant 2019R1A2C2087604) and Creative Materials Discovery Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (grant 2018M3D1A1058536). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2022R1C1C1006337). Experiments at PLS were partly supported by MEST and POSTECH. Publisher Copyright: © 2022 American Chemical Society. All rights reserved.

PY - 2022/10/5

Y1 - 2022/10/5

N2 - To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-To-cycle and device-To-device variations. This study experimentally demonstrated and compared the filamentary and interface-Type resistive switching (RS) behaviors of tantalum oxide (Ta2O5and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5is a strong candidate for memristor, the filament-Type RS behavior of Ta2O5does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-Type TaO2memristor and compared it to a filament type of Ta2O5memristor to secure the weight update characteristics and reliability. The TaO2-based interface-Type memristor exhibited gradual RS characteristics and area dependency in both high-and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-Type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-Term potentiation (LTP) and long-Term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

AB - To implement artificial neural networks (ANNs) based on memristor devices, it is essential to secure the linearity and symmetry in weight update characteristics of the memristor, and reliability in the cycle-To-cycle and device-To-device variations. This study experimentally demonstrated and compared the filamentary and interface-Type resistive switching (RS) behaviors of tantalum oxide (Ta2O5and TaO2)-based devices grown by atomic layer deposition (ALD) to propose a suitable RS type in terms of reliability and weight update characteristics. Although Ta2O5is a strong candidate for memristor, the filament-Type RS behavior of Ta2O5does not fit well with ANNs demanding analog memory characteristics. Therefore, this study newly designed an interface-Type TaO2memristor and compared it to a filament type of Ta2O5memristor to secure the weight update characteristics and reliability. The TaO2-based interface-Type memristor exhibited gradual RS characteristics and area dependency in both high-and low-resistance states. In addition, compared to the filamentary memristor, the RS behaviors of the TaO2-based interface-Type device exhibited higher suitability for the neuromorphic, symmetric, and linear long-Term potentiation (LTP) and long-Term depression (LTD). These findings suggest better types of memristors for implementing ionic memristor-based ANNs among the two types of RS mechanisms.

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Filamentary and Interface-Type Memristors Based on Tantalum Oxide for Energy-Efficient Neuromorphic Hardware

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