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General Information
Editor-in-chief

Prof. Pascal Lorenz
University of Haute Alsace, France
 
It is my honor to be the editor-in-chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.

IJFCC 2013 Vol.2(5): 407-412 ISSN: 2010-3751
DOI: 10.7763/IJFCC.2013.V2.195

Adaptive Segmentation Gaussian Mixtures Models for Approximating to Drastically Scaled-Various Sloped Long-Tail RTN Distributions

Worawit Somha and Hiroyuki Yamauchi
Abstract—This paper proposes a fitting method to approximate the mixtures of various sloped-tail Gamma distribution characterizing the random telegraph noises (RTN) by an adaptive segmentation Gaussian mixtures model (GMM). The concepts central to the proposed method are 1) adaptive segmentation of the long-heavy tailed distributions such that the log-likelihood of GMM in each partition is maximized and 2) copy and paste with an adequate weight into each partition. This allows the fitting model to apply various bounded tail distribution even with multiple convex and concave folding curves. It is verified that the proposed method can reduce the error of the fail-bit predictions by 2-orders of magnitude while reducing the iterations for EM step convergence to 1/16 at the interest point of the fail probability of 10-12 which corresponds to the design point to realize a 99.9% yield of 1Gbit chips.

Index Terms—Mixtures of Gaussian, random telegraph noise, em algorithm, heavy-tail distribution, long-tail distribution, fail-bit analysis, static random access memory, guard band design.

The authors are with the Information Intelligent System Fukuoka Institute of Technology, 3-30-1, Wajiro-Higashi, Higashi-ku, Fukuoka, Japan (e-mail: bd12002@ bene.fit.ac.jp, yamauchi@fit.ac.jp).

[PDF]

Cite: Worawit Somha and Hiroyuki Yamauchi, "Adaptive Segmentation Gaussian Mixtures Models for Approximating to Drastically Scaled-Various Sloped Long-Tail RTN Distributions," International Journal of Future Computer and Communication vol. 2, no. 5, pp. 407-412, 2013.

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