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A Segmentation Kernel Fitting Technique to Circumvent Extreme Deviation from Exponentially Descent Tail Distribution

Worawit Somha 1 and Hiroyuki Yamauchi 2
1. King Mongkut’s Institute of Technology, Ladkrabang, Bangkok, Thailand
2. Fukuoka Institute of Technology, Wajiro-Higashi, Higashi-ku, Fukuoka, Japan
Abstract—A segmentation kernel fitting technique has been proposed to circumvent an extreme deviation from the exponentially steeping descent tail distribution in the deconvolution. The proposed technique regenerates each segmented distribution line by finding the minimum of unconstrained multivariable function using derivative-free method. We decomposed the convolution effects of the two types of the minimum operating voltage variations caused by the spatially random threshold variation (VDDSPAT) and the temporally random threshold variation (VDDTIME), respectively. We discussed the VDDSPAT and VDDTIME effects on the SRAM fail-bit count (FBC) based on the decomposing results. It is found that the FBC estimation error for the proposed one can be reduced to almost 14-orders of magnitude smaller than that for the off-the-shell functions. 
 
Index Terms—deconvolution, Random telegraph noise, MATLAB-deconvolution function, SRAM margin variation

Cite: Worawit Somha and Hiroyuki Yamauchi, "A Segmentation Kernel Fitting Technique to Circumvent Extreme Deviation from Exponentially Descent Tail Distribution," International Journal of Electrical and Electronic Engineering & Telecommunications, Vol. 7, No. 3, pp. 114-118, July 2018. Doi: 10.18178/ijeetc.7.3.114-118
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