Search Results - "Evolution"

Guide to state-of-the-art electron devices

Table of Contents: “…Machine generated contents note: Foreword Preface Contributors and Acknowledgements Historic Timeline Part I - Basic Electron Devices 1 Bipolar Transistors 1.1 Motivation 1.2 The pn Junction and Its Electronic Applications 1.3 The Bipolar Junction Transistor and Its Electronic Applications 1.4 Optimization of Bipolar Transistors 1.5 SiGe Heterojunction Bipolar Transistors References 2 MOS Devices 2.1 Introduction 2.2 MOSFET Basics 2.3 The Evolution of MOSFET 2.4 Concluding Remarks References 3 Memory Devices 3.1 Introduction 3.2 Volatile Memories 3.3 Non-Volatile Memories 3.4 Future Perspectives of MOS Memories 3.5 Closing Remarks References 4 Passive Components 4.1 Discrete and integrated passive components 4.2 Application in Analog ICs and DRAM 4.3 The planar Spiral Inductor - A Case Study 4.4 Parasitics in Integrated Circuits References 5 Emerging Research Devices 5.1 Non-Charge Based Switching 5.2 Carbon as a Replacement for Silicon and the Rise of Moletronics 5.3 Conclusions References Part II - Aspects of Device and IC Manufacturing 6 Electronics Materials 6.1 Introduction 6.2 Silicon Device Technology 6.3 Compound Semiconductor Devices 6.4 Electronic Displays 6.5 Conclusions References 7 Compact Modeling 7.1 The Role of Compact Models 7.2 Bipolar Transistor Compact Modeling 7.3 MOS Transistor Compact Modeling 7.4 Compact Modeling of Passive Components 7.5 Benchmarking and Implementation References 8 Technology Computer Aided Design 8.1 Introduction 8.2 Drift-Diffusion Model 8.3 Microscopic Transport Models 8.4 Quantum Transport Models 8.5 Process and Equipment Simulation References 9 Device Reliability Physics 9.1 Introduction and Background 9.2 Device Reliability Issues 9.3 Interconnect Degradation Mechanisms 9.4 Circuit-Level Reliability Issues 9.5 Microscopic Approaches to Assuring Reliability of ICs References 10 Semiconductor Manufacturing 10.1 Introduction 10.2 Substrates 10.3 Lithography and Etching 10.4 Front-End Processing 10.5 Back-End Processing 10.6 Process Control 10.7 Assembly and Test 10.8 Future Directions References Part III - Applications based on Electron Devices 11 VLSI Logic Technology and Circuits 11.1 Introduction 11.2 MOSFET Scaling Trends 11.3 Low-Power and High-Speed Logic Design 11.4 Scaling-Driven technology Enhancements 11.5 Ultra-Low Voltage Transistors 11.6 Interconnects 11.7 Memory Design 11.8 System Integration References 12 VLSI Mixed-Signal Technology And Circuits 12.1 Introduction 12.2 Analog/Mixed-Signal Technologies in Scaled CMOS 12.3 Data Converter ICs 12.4 Mixed-Signal Circuits in Low-Power Display Applications 12.5 Image Sensor Technology and Circuits References 13 Memory Technologies 13.1 Semiconductor Memory History 13.2 State of Mainstream Semiconductor Memory Today 13.3 Emerging Memory Technologies 13.4 Conclusions References 14 RF&Microwave Semiconductor Technologies 14.1 III-V Based: GaAs and InP 14.2 Si and SiGe 14.3 Wide Bandgap Devices (Group III-Nitrides, SiC and Diamond) References 15 Power Devices and ICs 15.1 Overview of Power Devices & ICs 15.2 Two-Carrier and High-Power Devices 15.3 Power MOSFET Devices 15.4 High-Voltage and Power ICs 15.5 Wide Bandgap Power Devices References 16 Photovoltaic Device Applications 16.1 Introduction 16.2 Silicon Photovoltaics 16.3 Polycrystalline Thin-Film Photovoltaics 16.4 III-V Compound Photovoltaics 16.5 Future Concepts in Photovoltaics References 17 Large Area Electronics 17.1 Thin-Film Solar Cells 17.2 Large-Area Imaging 17.3 Flat-Panel Displays References 18 Microelectromechanical Systems (MEMS) 18.1 Introduction 18.2 The 1960's - First Micromachined Structures Envisioned 18.3 The 1970's - Integrated Sensors Started 18.4 The 1980's - Surface Micromachining Emerged 18.5 The 1990's - MEMS Impacted Various Fields 18.6 The 2000's - Diversified Sophisticated Systems Enabled By MEMS 18.7 Future Outlook References 19 Vacuum Device Applications 19.1 Traveling-Wave Devices 19.2 Klystrons 19.3 Inductive Output Tubes 19.4 Crossed-Field Devices 19.5 Gyro-Devices References 20 Optoelectronic Device Applications 20.1 Introduction 20.2 Light Emission in Semiconductors 20.3 Photodetectors 20.4 Integrated Optoelectronics 20.5 Optical Interconnects 20.6 Concluding Remarks References 21 Devices for the Post Silicon CMOS Era 21.1 Introduction 21.2 Devices for the 8-nm Node With Conventional Materials 21.3 New Channel Materials and Devices 21.4 Concluding Remarks References Index.…”
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A Guide for Machine Vision in Quality Control by Anand, Sheila

Table of Contents: “…Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Authors -- 1: Computer and Human Vision Systems -- 1.1 The Human Eye -- 1.2 Computer versus Human Vision Systems -- 1.3 Evolution of Computer Vision -- 1.4 Computer/Machine Vision and Image Processing -- 1.5 Applications of Computer Vision -- 1.6 Summary -- Exercises -- 2: Digital Image Fundamentals -- 2.1 Digital Image -- 2.2 Monochrome and Color Images -- 2.3 Image Brightness and Contrast -- 2.4 2D, 3D, and 4D Images -- 2.5 Digital Image Representation -- 2.6 Digital Image File Formats…”
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Statistical monitoring of complex multivariate processes with applications in industrial process control / by Krüger, Uwe, Dr

Table of Contents: “…Machine generated contents note: Preface Introduction I Fundamentals of Multivariate Statistical Process Control 1 Motivation for Multivariate Statistical Process Control 1.1 Summary of Statistical Process Control 1.1.1 Roots and Evolution of Statistical Process Control 1.1.2 Principles of Statistical Process Control 1.1.3 Hypothesis Testing, Type I and II errors 1.2 Why Multivariate Statistical Process Control 1.2.1 Statistically Uncorrelated Variables 1.2.2 Perfectly Correlated Variables 1.2.3 Highly Correlated Variables 1.2.4 Type I and II Errors and Dimension Reduction 1.3 Tutorial Session 2 Multivariate Data Modeling Methods 2.1 Principal Component Analysis 2.1.1 Assumptions for Underlying Data Structure 2.1.2 Geometric Analysis of Data Structure 2.1.3 A Simulation Example 2.2 Partial Least Squares 2.2.1 Assumptions for Underlying Data Structure 2.2.2 Deflation Procedure for Estimating Data Models 2.2.3 A Simulation Example 2.3 Maximum Redundancy Partial Least Squares 2.3.1 Assumptions for Underlying Data Structure 2.3.2 Source Signal Estimation 2.3.3 Geometric Analysis of Data Structure 2.3.4 A Simulation Example 2.4 Estimating the Number of Source Signals 2.4.1 Stopping Rules for PCA Models 2.4.2 Stopping Rules for PLS Models 2.5 Tutorial Session 3 Process Monitoring Charts 3.1 Fault Detection 3.1.1 Scatter Diagrams 3.1.2 Nonnegative Quadratic Monitoring Statistics 3.2 Fault Isolation and Identification 3.2.1 Contribution Charts 3.2.2 Residual-Based Tests 3.2.3 Variable Reconstruction 3.3 Geometry of Variable Projections 3.3.1 Linear Dependency of Projection Residuals 3.3.2 Geometric Analysis of Variable Reconstruction 3.4 Tutorial Session II Application Studies 4 Application to a Chemical Reaction Process 4.1 Process Description 4.2 Identification of a Monitoring Model 4.3 Diagnosis of a Fault Condition 5 Application to a Distillation Process 5.1 Process Description 5.2 Identification of a Monitoring Model 5.3 Diagnosis of a Fault Condition III Advances in Multivariate Statistical Process Control 6 Further Modeling Issues 6.1 Accuracy of Estimating PCA Models 6.1.1 Revisiting the Eigendecomposition of Sz0z0 6.1.2 Two Illustrative Examples 6.1.3 Maximum Likelihood PCA for Known Sgg 6.1.4 Maximum Likelihood PCA for Unknown Sgg 6.1.5 A Simulation Example 6.1.6 A Stopping Rule for Maximum Likelihood PCA Models 6.1.7 Properties of Model and Residual Subspace Estimates 6.1.8 Application to a Chemical Reaction Process - Revisited 6.2 Accuracy of Estimating PLS Models 6.2.1 Bias and Variance of Parameter Estimation 6.2.2 Comparing Accuracy of PLS and OLS Regression Models 6.2.3 Impact of Error-in-Variables Structure upon PLS Models 6.2.4 Error-in-Variable Estimate for Known See 6.2.5 Error-in-Variable Estimate for Unknown See 6.2.6 Application to a Distillation Process - Revisited 6.3 Robust Model Estimation 6.3.1 Robust Parameter Estimation 6.3.2 Trimming Approaches 6.4 Small Sample Sets 6.5 Tutorial Session 7 Monitoring Multivariate Time-Varying Processes 7.1 Problem Analysis 7.2 Recursive Principal Component Analysis 7.3 MovingWindow Principal Component Analysis 7.3.1 Adapting the Data Correlation Matrix 7.3.2 Adapting the Eigendecomposition 7.3.3 Computational Analysis of the Adaptation Procedure 7.3.4 Adaptation of Control Limits 7.3.5 Process Monitoring using an Application Delay 7.3.6 MinimumWindow Length 7.4 A Simulation Example 7.4.1 Data Generation 7.4.2 Application of PCA 7.4.3 Utilizing MWPCA based on an Application Delay 7.5 Application to a Fluid Catalytic Cracking Unit 7.5.1 Process Description 7.5.2 Data Generation 7.5.3 Pre-analysis of Simulated Data 7.5.4 Application of PCA 7.5.5 Application of MWPCA 7.6 Application to a Furnace Process 7.6.1 Process Description 7.6.2 Description of Sensor Bias 7.6.3 Application of PCA 7.6.4 Utilizing MWPCA based on an Application Delay 7.7 Adaptive Partial Least Squares 7.7.1 Recursive Adaptation of Sx0x0 and Sx0y0 7.7.2 MovingWindow Adaptation of Sv0v0 and Sv0y0 7.7.3 Adapting The Number of Source Signals 7.7.4 Adaptation of the PLS Model 7.8 Tutorial Session 8 Monitoring Changes in Covariance Structure 8.1 Problem Analysis 8.1.1 First Intuitive Example 8.1.2 Generic Statistical Analysis 8.1.3 Second Intuitive Example 8.2 Preliminary Discussion of Related Techniques 8.3 Definition of Primary and Improved Residuals 8.3.1 Primary Residuals for Eigenvectors 8.3.2 Primary Residuals for Eigenvalues 8.3.3 Comparing both Types of Primary Residuals 8.3.4 Statistical Properties of Primary Residuals 8.3.5 Improved Residuals for Eigenvalues 8.4 Revisiting the Simulation Examples in Section 8.1 8.4.1 First Simulation Example 8.4.2 Second Simulation Example 8.5 Fault Isolation and Identification 8.5.1 Diagnosis of Step-Type Fault Conditions 8.5.2 Diagnosis of General Deterministic Fault Conditions 8.5.3 A Simulation Example 8.6 Application Study to a Gearbox System 8.6.1 Process Description 8.6.2 Fault Description 8.6.3 Identification of a Monitoring Model 8.6.4 Detecting a Fault Condition 8.7 Analysis of Primary and Improved Residuals 8.7.1 Central Limit Theorem 8.7.2 Further Statistical Properties of Primary Residuals 8.7.3 Sensitivity of Statistics based on Improved Residuals 8.8 Tutorial Session IV Description of Modeling Methods 9 Principal Component Analysis 9.1 The Core Algorithm 9.2 Summary of the PCA Algorithm 9.3 Properties of a PCA Model 10 Partial Least Squares 10.1 Preliminaries 10.2 The Core Algorithm 10.3 Summary of the PLS Algorithm10.4 Properties of PLS 10.5 Properties of Maximum Redundancy PLS References Index.…”
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Encyclopedia of Caribbean archaeology /

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PEM fuel cells thermal and water management fundamentals / by Wang, Yun

Table of Contents: “…Thermal transport and management -- 8.1 Heat transfer overview -- 8.1.1 Heat transfer and its importance -- 8.1.2 Heat transfer modes -- 8.1.2.1 Heat conduction -- 8.1.2.2 Convective heat transfer -- 8.1.2.3 Heat radiation -- 8.1.3 Heat transfer in porous media -- 8.2 Heating mechanisms -- 8.2.1 The entropic heat -- 8.2.2 Irreversibility of the electrochemical reactions -- 8.2.3 The Joules heat -- 8.3 Steady-state heat transfer -- 8.3.1 One-dimensional (1D) heat transfer analysis -- 8.3.2 Two-dimensional (2D) heat transfer analysis -- 8.3.3 Numerical analysis -- 8.3.3.1 Macroscopic model prediction -- 8.3.3.2 Pore-level heat transfer -- 8.4 Transient phenomena -- 8.4.1 General transient operation -- 8.4.2 Transient subfreezing operation -- 8.4.2.1 Temperature evolution and voltage loss -- 8.4.2.2 Activation voltage loss -- 8.4.2.3 Ohmic voltage loss -- 8.5 Experimental measurement of thermal conductivity -- 8.6 Cooling methods -- 8.6.1 Heat spreaders cooling -- 8.6.2 Cooling by air or liquid flow -- 8.6.3 Phase-change-based cooling -- 8.7 Example: a thermal system of automotive fuel cells -- 8.7.1 A lumped-system model of a PEM fuel cell -- 8.7.2 Bypass valve -- 8.7.3 Radiator -- 8.7.4 Transport delay -- 8.7.5 Fluid mixer -- 8.7.6 Cathode intercooler -- 8.7.7 Anode heat exchanger -- 8.8 Chapter summary --…”
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Aerospace sensors by Nebylov, A. V. (Aleksandr Vladimirovich)

Table of Contents: “…Autonomous radio sensors for motion parameters -- 4.1 Introduction -- 4.2 Doppler sensors for ground speed and crab angle -- 4.2.1 Physical basis and functions -- 4.2.2 Principle of operation -- 4.2.3 Classification and features of sensors for ground speed and crab angle -- 4.2.4 Generalized structural diagram for the ground speed and crab angle meter -- 4.2.5 Design principles -- 4.2.6 Sources of Doppler radar errors -- 4.2.7 Examples -- 4.3 Airborne weather sensors -- 4.3.1 Weather radar as mandatory equipment of airliners and transport aircraft -- 4.3.2 Multifunctionality of airborne weather radar -- 4.3.3 Meteorological functions of AWR -- 4.3.4 Principles of DWP detection with AWR -- 4.3.4.1 Developing methods of DWP detection -- 4.3.4.2 Cumulonimbus clouds and heavy rain -- 4.3.4.3 Turbulence detection -- 4.3.4.4 Wind shear detection -- 4.3.4.5 Hail zone detection -- 4.3.4.6 Probable icing-in-flight zone detection -- 4.3.5 Surface mapping -- 4.3.5.1 Comparison of radar and visual orientation -- 4.3.5.2 The surface-mapping principle -- 4.3.5.3 Reflecting behavior of the earth's surface -- 4.3.5.4 The radar equation and signal correction -- 4.3.5.5 Automatic classification of navigational landmarks -- 4.3.6 AWR design principles -- 4.3.6.1 The operating principle and typical structure of AWR -- 4.3.6.2 AWR structures -- 4.3.6.3 Performance characteristics: basic requirements -- 4.3.7 AWR examples -- 4.3.8 Lightning sensor systems: stormscopes -- 4.3.9 Optical radar -- 4.3.9.1 Doppler lidar -- 4.3.9.2 Infrared locators and radiometers -- 4.3.10 The integrated localization of dangerous phenomena -- 4.4 Collision avoidance sensors -- 4.4.1 Traffic alert and collision avoidance systems (TCAS) -- 4.4.1.1 The purpose -- 4.4.1.2 A short history -- 4.4.1.3 TCAS levels of capability -- 4.4.1.4 TCAS concepts and principles of operation -- 4.4.1.5 Basic components -- 4.4.1.6 Operation -- 4.4.1.7 TCAS logistics -- 4.4.1.8 Cockpit presentation -- 4.4.1.9 Examples of system implementation -- 4.4.2 The ground proximity warning system (GPWS) -- 4.4.2.1 Purpose and necessity -- 4.4.2.2 GPWS history, principles, and evolution -- 4.4.2.3 GPWS modes -- 4.4.2.4 Shortcomings of classical GPWS -- 4.4.2.5 Enhanced GPWS -- 4.4.2.6 Look-ahead warnings -- 4.4.2.7 Implementation examples -- References --…”
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Load testing of bridges : current practice and diagnostic load testing /

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Taming the beast Wall Street's imperfect answers to making money / by Light, Larry, 1959-

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State territory and international law / by Ezenwajiaku, Josephat Chukwuemeka

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Emerging Devices for Low-Power and High-Performance Nanosystems : Physics, Novel Functions, and Data Processing /

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Lexicon of pulse crops / by Mikić, Aleksandar

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Truth, Silence and Violence in Emerging States : Histories of the Unspoken /

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Markov random flights / by Kolesnik, Alexander D.

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