Paper Info
Title
Data-Dependent Statistical Memory Model for Passive Array of Memristive Devices
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.
Year
DOI
Venue
2010
10.1109/TCSII.2010.2083191
IEEE Trans. on Circuits and Systems
Keywords
Field
DocType
memory array size,replica-based self-adaptable sense resistor,random-access storage,statistical analysis,optimum detection voltage margin,circuit model,computational efficiency,equivalent statistical circuit model,data pattern dependence,m passive memory array,nonvolatile resistive memory,large voltage detection window,sneak current,memristive device,memristive devices,broad range,random data distribution,memristors,voltage detection windowing,passive array,data-dependent statistical memory model,passive memory array,statistical model,low-power reading,memory model,mathematical model,data models,nonvolatile memory
Replica,Data modeling,Memristor,Computer science,Voltage,Electronic engineering,Resistor,Non-volatile memory,Memory model,Statistical model
Journal
Volume
Issue
ISSN
57
12
1549-7747
Citations 
PageRank 
References 
2
0.45
6
Authors
3
Name
Order
Citations
PageRank
Sang-Ho Shin142041.46
Kyungmin Kim216820.97
Sung-Mo Steve Kang31198213.14