Analysis of passive memristive devices array: Data-dependent statistical model and self-adaptable sense resistance for RRAMs

Sangho Shin, Kyungmin Kim, Sung Mo Kang

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

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 × 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 \times 128 passive array with off-to-on resistance ratio of 103.

Original languageEnglish (US)
Article number6053988
Pages (from-to)2021-2032
Number of pages12
JournalProceedings of the IEEE
Volume100
Issue number6
DOIs
StatePublished - Jun 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Electrical and Electronic Engineering

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