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  1. ECE 695A Lecture 30: Breakdown in Dielectrics with Defects

    08 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    Outline:IntroductionTheory of pre-existing defects: Thin oxidesTheory of pre-existing defects: thick oxidesConclusions

  2. ECE 695A Lecture 30R: Review Questions

    08 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    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...

  3. ECE 595E Lecture 30: Applications of CAMFR

    08 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

  4. Data-adaptive Filtering and the State of the Art in Image Processing

    15 Apr 2013 | Online Presentations | Contributor(s): Peyman Milanfar

    In this talk, I will present a practical and unified framework for understanding some common underpinnings of these methods. This leads to new insights and a broad understanding of how these diverse methods interrelate. I will also discuss the statistical performance of the resulting algorithms,...

  5. Random Forest Model Objects for Pulmonary Toxicity Risk Assessment

    17 Apr 2013 | Downloads | Contributor(s): Jeremy M Gernand

    This download contains MATLAB treebagger or Random Forest (RF) model objects created via meta-analysis of nanoparticle rodent pulmonary toxicity experiments. The ReadMe.txt file contains object descriptions including output definitions, input parameter descriptions, and applicable limits.

  6. ECE 595E Lecture 32: Simulations of Coupled Mode Theory Simulation (CMT)

    12 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

    Outline:Recap from FridayNumerical ODE solversInitial value problemsBoundary value problemsnanoHUB Tool – CMTcomb3:RationaleGoverning ODEsUser interfaceOutput analysis

  7. ECE 595E Lecture 33: Introduction to Finite-Difference Time-Domain Simulations

    12 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

    Outline:Recap from MondayIntroduction to FDTDSpecial features of MEEP:Perfectly matched layersSubpixel averagingSymmetryScheme (programmable) interfaceExamples:Periodic light-trapping structuresRandomly textured structures

  8. ECE 595E Lecture 34: Applications of Finite-Difference Time-Domain Simulations

    18 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

    Outline:Recap from WednesdayPeriodic and randomly textured light-trapping structuresOverviewExperimental motivationComputational setupSimulated field evolutionAbsorption spectraFront coatingsCorrelated random structures

  9. ECE 595E Lecture 35: MEEP Tutorial I

    18 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

    Outline:MEEP InterfacesMEEP ClassesTutorial examples:WaveguideBent waveguide

  10. ECE 595E Lecture 36: MEEP Tutorial II

    30 Apr 2013 | Online Presentations | Contributor(s): Peter Bermel

    Outline:Recap from MondayExamplesMultimode ring resonatorsIsolating individual resonancesKerr nonlinearitiesQuantifying third-harmonic generation

  11. ECE 695A Lecture 31: Collecting and Plotting Data

    15 Apr 2013 | Online Presentations | 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

  12. ECE 695A Lecture 31A: Appendix - Bootstrap Method Introduction

    15 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

  13. ECE 695A Lecture 31R: Review Questions

    15 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    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...

  14. ECE 695A Lecture 32: Physical vs. Empirical Distribution

    17 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    Outline:Physical Vs. empirical distributionProperties of classical distribution functionMoment-based fitting of dataConclusions

  15. ECE 695A Lecture 32R: Review Questions

    17 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    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...

  16. Integrated Imaging Seminar Series

    30 Apr 2013 | Series | Contributor(s): Charles Addison Bouman

    Integrated imaging seminar series is jointly sponsored by the Birck Nanotechnology Center and ECE. Integrated Imaging is defined as a cross-disciplinary field combining sensor science, information processing, and computer systems for the creation of novel imaging and sensing systems. In this...

  17. Buckypaper

    17 Apr 2013 | Presentation Materials | Contributor(s): shaheen goel

    the presentation gives a basic idea about the buckypaper and give breif details about the synthesis properties and applications of buckypaper

  18. ECE 695A Lecture 33: Model Selection/Goodness of Fit

    18 Apr 2013 | Online Presentations | 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

  19. ECE 695A Lecture 33R: Review Questions

    18 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    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...

  20. ECE 695A Lecture 34: Scaling Theory of Design of Experiments

    18 Apr 2013 | Online Presentations | Contributor(s): Muhammad Alam

    Outline:IntroductionBuckingham PI TheoremAn Illustrative ExampleRecall the scaling theory of HCI, NBTI, and TDDBConclusions

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