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

作　　者：张江伟 汤振森 许诺 王耀 孙红辉 王之元 方粮 

机构地区：[1]State Key Laboratory of High Performance Computing,National University of Defense Technology [2]School of Computer,National University of Defense Technology [3]Department of Electrical and Computer Engineering,University of Pittsburgh [4]Department of Material Science and Engineering,College of Engineering,Seoul National University [5]School of Computer National University of Defense Technology

出　　处：《Chinese Physics B》2017年第9期70-81,共12页中国物理B（英文版）

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

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.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 

分 类 号：TN60[电子电信—电路与系统] TP391.41[自动化与计算机技术—计算机应用技术]