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ECE 695A Lecture 26-2: Statistics of Soft Breakdown (Breakdown Position correlation)
28 Mar 2013 | | Contributor(s):: Muhammad Alam
Outline:Position and time correlation of BD spotHow to determine the position of the BD SpotPosition correlation in BD spotsWhy is localization so weak?Conclusions
ECE 695A Lecture 26R: Review Questions
ECE 695A Lecture 27: Correlated TDDB in Off-State HCI
29 Mar 2013 | | Contributor(s):: Muhammad Alam
ECE 695A Lecture 27R: Review Questions
ECE 695A Lecture 28: Circuit Implications of Dielectric Breakdown
08 Apr 2013 | | Contributor(s):: Muhammad Alam
Outline:Part 1 - Understanding Post-BD FET behaviorBD position determinationHard and Soft BD in FETsDistinguishing leakage and intrinsic FET parameters shiftsPart 2 - Impact of breakdown on digital circuit operationBD in ring oscillatorBDinSR AMcellTiming, BD into soft node
ECE 695A Lecture 29: Breakdown of Thick Dielectrics
Outline:IntroductionSpatial and temporal dynamics during breakdownBreakdown in bulk oxides: puzzleConclusions
ECE 695A Lecture 29A: Appendix - Dimension of a Surface
ECE 695A Lecture 29R: Review Questions
Review Questions:Mention a few differences between thick and thin oxide breakdown.Is breakdown in thick oxides contact dominated? Can I use AHI theory here?How does the Paschen’s cascade initiate?What does it mean to have a fractal dimension of 1.7 for 2D breakdown? Why does the number suggest...
ECE 695A Lecture 2: A Brief History of Reliability and Types of Reliability Models
16 Jan 2013 | | Contributor(s):: Muhammad Alam
Outline:Reliability as a General PhenomenaA Brief History of ReliabilityApproaches to Reliability PhysicsConclusions
ECE 695A Lecture 30: Breakdown in Dielectrics with Defects
Outline:IntroductionTheory of pre-existing defects: Thin oxidesTheory of pre-existing defects: thick oxidesConclusions
ECE 695A Lecture 30R: Review Questions
Outline:What is the difference between extrinsic vs. intrinsic breakdown?Does gas dielectric have extrinsic breakdown? Why or why not?What does ESD damage and the plasma damage to thin oxides?Can you explain the physical meaning of infant mortality ? How does it relate to yield of semiconductor...
ECE 695A Lecture 31: Collecting and Plotting Data
15 Apr 2013 | | Contributor(s):: Muhammad Alam
Outline:Origin of data, Field Acceleration vs. Statistical InferenceNonparametric informationPreparing data for projection: Hazen formula Preparing data for projection: Kaplan formulaConclusion
ECE 695A Lecture 31A: Appendix - Bootstrap Method Introduction
ECE 695A Lecture 31R: Review Questions
Review Questions:What is the difference between parametric estimation vs. non-parametric estimation?What principle did Tacho Brahe’s approach assume?What is the difference between population and sample? When we collect data for TDDB or NBTI, what type of data are we collecting?What problem does...
ECE 695A Lecture 32: Physical vs. Empirical Distribution
17 Apr 2013 | | Contributor(s):: Muhammad Alam
Outline:Physical Vs. empirical distributionProperties of classical distribution functionMoment-based fitting of dataConclusions
ECE 695A Lecture 32R: Review Questions
Review Questions:Why do people use Normal, log-normal, Weibull distributions when they do not know the exact physical distribution?What is the problem of using empirical distributions? What are the advantages?If you must choose an empirical distribution, what should be your criteria? (Nos. of...
ECE 695A Lecture 33: Model Selection/Goodness of Fit
18 Apr 2013 | | Contributor(s):: Muhammad Alam
Outline:The problem of matching data with theoretical distributionParameter extractions: Moments, linear regression, maximum likelihoodGoodness of fit: Residual, Pearson, Cox, AkikaConclusion
ECE 695A Lecture 33R: Review Questions
Review Questions:With higher number of model parameters, you can always get a good fit – why should you minimize the number of parametersLeast square method is a subset of maximum likelihood approach to data fitting. Is this statement correct?What aspect of the distribution function does...
ECE 695A Lecture 34: Scaling Theory of Design of Experiments
Outline:IntroductionBuckingham PI TheoremAn Illustrative ExampleRecall the scaling theory of HCI, NBTI, and TDDBConclusions
ECE 695A Lecture 34A: Appendix - Variability by Bootstrap Method