Somha, Worawit

Preferred name

Somha, Worawit

Main Affiliation

School of Industrial Education and Technology

worawit.so@kmitl.ac.th

Now showing 1 - 6 of 6

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A filter design for blind deconvolution to decouple unknown RDF/RTN factors from complexly coupled SRAM margin variations
(2016-04-11)
Yamauchi, Hiroyuki
;
Somha, Worawit
This paper demonstrates a blind deconvolution technique for decoupling the two variation factors caused by the Random Telegraph Noise (RTN) and the Random Dopant Fluctuation (RDF). Unlike the non-blind deconvolution, the blind deconvolution has to seek both of the two unknown factors for RTN and RDF simultaneously, given only the information about the overall SRAM margin distribution. This paper proposes a new filter design technique for the Richardson-Lucy (R-L) blind deconvolutions. This allows to enjoy the benefits of the R-L algorithm while avoiding the inherent pitfall or ringing errors even in the blind deconvolution. The relative errors of the blind-deconvolution for RDF and RTN are reduced to less than 1% within only 300-iteration cycles. This is 400-times shorter than the conventional one.
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Ringing error prevention techniques in Lucy-Richardson deconvolution process for SRAM space-time margin variation effect screening designs
(2015-05-05)
Yamauchi, Hiroyuki
;
Somha, Worawit
This paper proposes a ringing error avoidance technique in Lucy-Richardson-deconvolution (L-R-Dcnv) process, which is used for inversely analyzing the Random Telegraph Noise (RTN) effects on overall SRAM margin variations. The proposed ringing prevention technique successfully circumvents the ringing error by reducing the phase difference between the feedback-gain and deconvolution target distributions in L-R-Dcnv iteration cycles. This avoids any unwanted positive feedbacks, resulting in no error amplification. This effectiveness has been demonstrated with applying it to a real L-R-Dcnv analysis for the effects of the RTN on the overall SRAM margin variations, while exploiting a quicker convergence benefit of L-R-Dcnv algorithm. It has been shown that the proposed technique reduces its relative errors of the RTN deconvolution by 102~103 times compared with the conventional L-R-Dcnv. This enables to increase an accuracy of the fail-bit-count prediction based on the cumulative density function (cdf) of the convolution of the RTN with the Random Dopant Fluctuation (RDF) by over 2-orders of magnitude while accelerating its convergence speed by 7~30 times of the conventional one.
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A parallel filter technique to stabilize error-rectification behavior in RDF deconvolution process for SRAM screening test
(2016-08-29)
Yamauchi, Hiroyuki
;
Somha, Worawit
This paper proposes a parallel filter design technique for stabilizing the error rectification process in the iterative Lucy-Richardson (LR) deconvolution. The proposed filter is designed to adequately decouple the variation factors caused by the Random Dopant Fluctuation (RDF) from the complexly coupled SRAM margin variations. This allows to enjoy the benefits of the LR algorithm while avoiding the inherent pitfall or ringing behaviors even in the RDF deconvolution. The ringing elimination with the proposed filter contributes to suppress the relative errors to 7-orders of magnitude smaller than that for the conventional single filter. When compared with the conventional single filter at the number of iteration cycles of 30, a 100-fold larger error reduction is achieved.
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A segmentation kernel fitting technique to circumvent extreme deviation from exponentially descent tail distribution
(2018-07-01)
Somha, Worawit
;
Yamauchi, Hiroyuki
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 (VDD<inf>SPAT</inf>) and the temporally random threshold variation (VDD<inf>TIME</inf>), respectively. We discussed the VDD<inf>SPAT</inf> and VDD<inf>TIME</inf> 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.
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An Error Reduction Technique in Richardson-Lucy Deconvolution Method
(2018-07-04)
Yamauchi, Hiroyuki
;
Somha, Worawit
An error reduction technique for Richardson-Lucy deconvolution (RL-deconv) is proposed. The deconvolution is indispensable technique for inversely analysing the SRAM fail-bit probability variations caused by the Random Telegraph Noise (RTN). The proposed technique reduces the phase difference between the two distributions of the deconvoluted RTN and the feedback-gain in the maximum likelihood (MLE) gradient iteration cycles. This avoids an unwanted positive feedback, resulting in a significant decrease in probability of undesired ringing occurrence. A quicker convergence benefit of the RL-deconv algorithm while avoiding the ringing is achieved. It has been demonstrated that the proposed technique reduces its relative deconvolution errors by 100 times compared with the conventional RL-deconv. This provides an increase in accuracy of the fail-bit-count prediction by over 2-orders of magnitude while accelerating its convergence speed by 33times of the conventional one.
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A dual-band filter designed for retrieving both left- and right-tailed RTN distributions in an iterative deconvolution procedure
(2025-05-01)
Yamauchi, Hiroyuki
;
Somha, Worawit
This paper proposes a dual-band filter design to alleviate a crucial ringing issue of the Richardson–Lucy deconvolution algorithm (RL-deconv). We found that the RL-deconv doesn’t work for an exponentially decaying tail due to the ringing. The reasons why we must handle this issue in the VLSI chip reliability design are: (1) the VLSI chip bit density has increased up to a 1012-bit scale, making the fail probability obey the long tail down to 10–12, and (2) the VLSI chip margin variations have become prominent, caused by atomic-level random behaviors. The tail for the variations caused margin variations to obey the Gamma distributions. Consequently, the tail of the VLSI chip margin distribution doesn’t follow the Gaussian distribution anymore. These backgrounds compel us to newly adopt the inverse problem methods to predict the tail distribution based on the deconvolution. As the tail gets longer, the element-wise misalignment becomes larger between the corresponding elements of the feedback gain and the objective of deconvolution, and this causes to more critical wrong element-wise amplification, leading to a ringing. We found that it is not sufficient to retrieve only the right tail because the left tail can no longer be ignored. The length of the left tail becomes long enough to influence the distribution after aging. To address this issue, this paper proposes a dual-band filter design for retrieving both left and right tails, which contributes to widening the alignment range of the feedback gain with the retrieving target in the RL-deconv iterative processes. It is found that the proposed technique reduces the RTN deconvolution error by 12-fold compared with the conventional one.

Somha, Worawit

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