Device-Parameter Estimation through IDDQ Signatures
نویسندگان
چکیده
منابع مشابه
Device-Parameter Estimation through IDDQ Signatures
We propose a novel technique for the estimation of deviceparameters suitable for postfabrication performance compensation and adaptive delay testing, which are effective means to improve the yield and reliability of LSIs. The proposed technique is based on Bayes’ theorem, in which the device-parameters of a chip, such as the threshold voltage of transistors, are estimated by current signatures ...
متن کاملAdaptive IDDQ: How to Set an IDDQ Limit for any Device Under Test
The combination of deep sub-micron technologies together with System on Chip complexity has brought the Device Under Test (DUT) Quiescent Supply Current (IDDQ ) into the Milliamps (mA) range. This IDDQ level, modulated by Electrical Parameters and Critical Dimensions Process spreads , makes almost impossible to detect the small current increase caused by the presence of a defect into the DUT. T...
متن کاملNew Graphical IDDQ Signatures Reduce Defect Level and Yield Loss
The measured IDDQ current as a function of vectors is defined here as the IDDQ signature of a chip. We examined the IDDQ signatures of a large number of SEMATECH chips that have been classified us good or bud by a combined decision from functional, delay and scan tests. We find that a single IDDQ threshold, whether absolute or differential, cannot separate good/bud chips with any desirable accu...
متن کاملGraphical IDDQ Signatures Reduce Defect Level and Yield Loss
We propose a new DDQ testing signature, the graphical DDQ signature. We discovered that noise, in the entire set of current measurements for a chip, is a vastly superior feature for classifying chips as good or bad, compared to present methods. The measured DDQ current as a function of vectors is defined here as the signature. We examine the shape of the waveform defined by the total set of the...
متن کاملBayesian parameter estimation through variational methods
We consider a logistic regression model with a Gaussian prior distribution over the parameters. We show that accurate variational techniques can be used to obtain a closed form posterior distribution over the parameters given the data thereby yielding a posterior predictive model. The results are readily extended to binary belief networks. For belief networks we also derive closed form posterio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2013
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e96.d.303