中国物理B ›› 2017, Vol. 26 ›› Issue (9): 90502-090502.doi: 10.1088/1674-1056/26/9/090502

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A generalized model of TiOx-based memristive devices andits application for image processing

Jiangwei Zhang(张江伟), Zhensen Tang(汤振森), Nuo Xu(许诺), Yao Wang(王耀), Honghui Sun(孙红辉), Zhiyuan Wang(王之元), Liang Fang(方粮)   

  1. 1 State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China;
    2 School of Computer, National University of Defense Technology, Changsha 410073, China;
    3 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA;
    4 Department of Material Science and Engineering, College of Engineering, Seoul National University, Seoul 151-744, Republic of Korea
  • 收稿日期:2017-03-08 修回日期:2017-05-18 出版日期:2017-09-05 发布日期:2017-09-05
  • 通讯作者: Liang Fang E-mail:lfang@nudt.edu.cn
  • 基金资助:

    Project supported by the National Natural Science Foundation of China (Grant Nos. 61332003 and 61303068) and the Natural Science Foundation of Hunan Province, China (Grant No. 2015JJ3024).

A generalized model of TiOx-based memristive devices andits application for image processing

Jiangwei Zhang(张江伟)1,2,3, Zhensen Tang(汤振森)1,2, Nuo Xu(许诺)1,2,4, Yao Wang(王耀)2, Honghui Sun(孙红辉)1,2, Zhiyuan Wang(王之元)1,2, Liang Fang(方粮)1,2   

  1. 1 State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China;
    2 School of Computer, National University of Defense Technology, Changsha 410073, China;
    3 Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA 15261, USA;
    4 Department of Material Science and Engineering, College of Engineering, Seoul National University, Seoul 151-744, Republic of Korea
  • Received:2017-03-08 Revised:2017-05-18 Online:2017-09-05 Published:2017-09-05
  • Contact: Liang Fang E-mail:lfang@nudt.edu.cn
  • Supported by:

    Project supported by the National Natural Science Foundation of China (Grant Nos. 61332003 and 61303068) and the Natural Science Foundation of Hunan Province, China (Grant No. 2015JJ3024).

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摘要：

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density, versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance (NDR) behavior. This model is physics-oriented and passes Linn's criteria. It not only exhibits sufficient accuracy (IV characteristics within 1.5% RMS), lower latency (below half the VTEAM model), and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression (LTP/LTD). Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy (for gray sketching and edge detection, respectively) compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

关键词: memristor modeling, memristor-based network, gray sketching, edge detection

Abstract:

Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density, versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiOx-based devices, which considers the negative differential resistance (NDR) behavior. This model is physics-oriented and passes Linn's criteria. It not only exhibits sufficient accuracy (IV characteristics within 1.5% RMS), lower latency (below half the VTEAM model), and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression (LTP/LTD). Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy (for gray sketching and edge detection, respectively) compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.

Key words: memristor modeling, memristor-based network, gray sketching, edge detection

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
87.85.dq (Neural networks) 95.75.Mn (Image processing (including source extraction)) 05.45.Tp (Time series analysis)