Data-dependent statistical memory model for passive array of memristive devices

Authors:
Sangho Shin;Kyungmin Kim;Sung-Mo Kang
Affiliations:
School of Engineering, University of California-Merced, Merced, CA;School of Engineering, University of California-Merced, Merced, CA;School of Engineering, University of California-Merced, Merced, CA
Venue:
IEEE Transactions on Circuits and Systems II: Express Briefs
Year:
2010

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Abstract

A 2 × 2 equivalent statistical circuit model is presented to deal with sneak currents and random data distributions for n × m passive memory arrays of memristive devices. The data-dependent 2 × 2 circuit model enables a broad range of analysis, such as the optimum detection voltage margin, with computational efficiency and has no limit on the memory array size. In addition, we propose replica-based self-adaptable sense resistors to achieve both low-power reading and large voltage detection windowing.