CAT: A Critical-Area-Targeted Test Set Modification Scheme for Reducing Launch Switching Activity in At-Speed Scan Testing

Author(s):  
K. Enokimoto ◽  
X. Wen ◽  
Y. Yamato ◽  
K. Miyase ◽  
H. Sone ◽  
...  
2010 ◽  
Vol E93-D (1) ◽  
pp. 2-9
Author(s):  
Kohei MIYASE ◽  
Xiaoqing WEN ◽  
Hiroshi FURUKAWA ◽  
Yuta YAMATO ◽  
Seiji KAJIHARA ◽  
...  

2011 ◽  
Vol E94-D (4) ◽  
pp. 833-840
Author(s):  
Yuta YAMATO ◽  
Xiaoqing WEN ◽  
Kohei MIYASE ◽  
Hiroshi FURUKAWA ◽  
Seiji KAJIHARA

2016 ◽  
Vol 25 (05) ◽  
pp. 1650040
Author(s):  
Ling Zhang ◽  
Jishun Kuang

Test power is one of the most challenges faced by Integrated Circuits. The author proposes a general scan chain architecture called Representative Scan (RS). It transforms the scan cells of conventional scan chain or sub-chain into circular shift registers and a representative flip-flop is chosen for each circular shift register, these representative flip-flops are connected serially to setup into the RS architecture. Thus, test data shifting path is shortened, then the switching activity is reduced in the shifting operates. The proposed scan architecture has the similar test power with the multiple scan chain, and only needs same test pins with single scan chain without added test pins. The experimental results show that the proposed scan architecture achieves very low shifting power. For benchmark circuits of ISCAS89, the shifting power of the best architecture of RS is only 0.53%–13.59% of the conventional scan. Especially for S35932, the shifting power on mintest test set is only 0.53% of the corresponding conventional scan. Compared with the conventional scan, the RS only needs to add a multiplexer for each scan cells, and the hardware cost is not high.


2019 ◽  
Vol 27 (1) ◽  
pp. 197-202
Author(s):  
Xiaoqing Wen ◽  
K. Enokimoto ◽  
K. Miyase ◽  
S. Kajihara ◽  
M. Aso ◽  
...  

2008 ◽  
Vol 24 (4) ◽  
pp. 379-391 ◽  
Author(s):  
Xiaoqing Wen ◽  
Kohei Miyase ◽  
Tatsuya Suzuki ◽  
Seiji Kajihara ◽  
Laung-Terng Wang ◽  
...  

1990 ◽  
Vol 29 (03) ◽  
pp. 167-181 ◽  
Author(s):  
G. Hripcsak

AbstractA connectionist model for decision support was constructed out of several back-propagation modules. Manifestations serve as input to the model; they may be real-valued, and the confidence in their measurement may be specified. The model produces as its output the posterior probability of disease. The model was trained on 1,000 cases taken from a simulated underlying population with three conditionally independent manifestations. The first manifestation had a linear relationship between value and posterior probability of disease, the second had a stepped relationship, and the third was normally distributed. An independent test set of 30,000 cases showed that the model was better able to estimate the posterior probability of disease (the standard deviation of residuals was 0.046, with a 95% confidence interval of 0.046-0.047) than a model constructed using logistic regression (with a standard deviation of residuals of 0.062, with a 95% confidence interval of 0.062-0.063). The model fitted the normal and stepped manifestations better than the linear one. It accommodated intermediate levels of confidence well.


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