Title :
Analysis of Passive Memristive Devices Array: Data-Dependent Statistical Model and Self-Adaptable Sense Resistance for RRAMs
Author :
Shin, Sangho ; Kim, Kyungmin ; Kang, Sung-Mo
Author_Institution :
Sch. of Eng., Univ. of California, Santa Cruz, CA, USA
fDate :
6/1/2012 12:00:00 AM
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 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.
Keywords :
equivalent circuits; memristors; random-access storage; statistical distributions; RRAM array; average current consumption reduction; data patterns; data-dependent statistical model; equivalent statistical circuit model; optimum detection voltage margin; passive memory arrays; passive memristive device array; random data distributions; resistive random access memory array; self-adaptable sense resistance; self-adaptable sense resistors; voltage detection margin; Arrays; Data models; Integrated circuit modeling; Mathematical model; Microprocessors; Random access memory; Resistors; Statistical analysis; Adaptable resistance; data pattern dependence; memristive devices; nonvolatile resistive memory; resistive random access memory (RRAM); statistical model;
Journal_Title :
Proceedings of the IEEE
DOI :
10.1109/JPROC.2011.2165690
