Cart (Loading....) | Create Account
Close category search window

Analysis of Passive Memristive Devices Array: Data-Dependent Statistical Model and Self-Adaptable Sense Resistance for RRAMs

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Sangho Shin ; Sch. of Eng., Univ. of California, Santa Cruz, CA, USA ; Kim, Kyungmin ; Kang, S.-M.S.

In this paper, a 2 × 2 equivalent statistical circuit model is presented to deal with sneak currents and random data distributions for design and analysis of n x m passive memory arrays of memristive devices. This data-dependent 2 × 2 model enables a broad range of analysis, such as the optimum detection voltage margin, with computational efficiency and no limit on the memory array size. We propose self-adaptable sense resistors that can find their statistical optimum values for reading stored data patterns by composing them with either a replica of a part of resistive random access memory (RRAM) array or a part of RRAM array itself. Self-adaptable resistors can increase the average voltage detection margin by 46%, and reduce the average current consumption by 14% for the case of a 128 × 128 passive array with OFF-to-ON resistance ratio of 103.

Published in:

Proceedings of the IEEE  (Volume:100 ,  Issue: 6 )

Date of Publication:

June 2012

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.