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http://dx.doi.org/10.5573/JSTS.2014.14.4.365

An Adaptively Segmented Forward Problem Based Non-Blind Deconvolution Technique for Analyzing SRAM Margin Variation Effects

Somha, Worawit (Fukuoka Institute of Technology)
Yamauchi, Hiroyuki (Fukuoka Institute of Technology)

Publication Information

JSTS:Journal of Semiconductor Technology and Science / v.14, no.4, 2014 , pp. 365-375 More about this Journal

Abstract

This paper proposes an abnormal V-shaped-error-free non-blind deconvolution technique featuring an adaptively segmented forward-problem based iterative deconvolution (ASDCN) process. Unlike the algebraic based inverse operations, this eliminates any operations of differential and division by zero to successfully circumvent the issue on the abnormal V-shaped error. This effectiveness has been demonstrated for the first time with applying to a real analysis for the effects of the Random Telegraph Noise (RTN) and/or Random Dopant Fluctuation (RDF) on the overall SRAM margin variations. It has been shown that the proposed ASDCN technique can reduce its relative errors of RTN deconvolution by $10^{13}$ to $10^{15}$ fold, which are good enough for avoiding the abnormal ringing errors in the RTN deconvolution process. This enables to suppress the cdf error of the convolution of the RTN with the RDF (i.e., fail-bit-count error) to $1/10^{10}$ error for the conventional algorithm.

Keywords

Non blind deconvolution; SRAM; random telegraph noise; time-dependent margin variation;

Citations & Related Records

Reference

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