Description
SPRINGER Analog Design Centering And Sizing by GRAEB HELMUT E.
What you'll find here is a fascinating compendium of fundamental problem formulations of analog design centering and sizing. This essential work provides a differentiated knowledge about the tasks of analog design centering and sizing. In particular, worst-case scenarios are formulated and analyzed. This work is right at the crossing point between process and design technology, and is both reference work and textbook for understanding CAD methods in analog sizing. 1. Introduction. 1.1 Integrated Circuits. 1.2 Analog Circuits. 1.3 Analog Design. 1.4 Analog Sizing. 2. Tolerance Design: Example. 2.1 RC Circuit. 2.2 Performance Evaluation. 2.3 Performance-Specification Features. 2.4 Nominal Design. 2.5 Yield Optimization/Design Centering. 3. Parameters & Tolerances, Performance & Specification. 3.1 Parameters. 3.2 Parameter Tolerances. 3.3 Range-Parameter Tolerances. 3.4 Statistical Parameter Distribution. 3.5 Univariate Normal Distribution. 3.6 Multivariate Normal Distribution. 3.7 Transformation of Statistical Distributions. 3.8 Generation of Normally Distributed Sample Elements. 3.9 Global and Local Parameter Tolerances. 3.10 Performance Features. 3.11 Numerical Simulation. 3.12 Performance-Specification Features 4. Analog Sizing Tasks. 4.1 Sensitivity-Based Analysis. 4.2 Performance-Sensitivity Computation. 4.3 Scaling Parameters and Performance Features. 4.4 Nominal Design. 4.5 Multiple-Objective Optimization. 4.6 Single-Objective Optimization. 4.7 Worst-Case Analysis and Optimization. 4.8 Yield Analysis, Yield Optimization/Design Centering. 5. Worst-Case Analysis. 5.1 Classical Worst-Case Analysis. 5.2 Realistic Worst-Case Analysis. 5.3 Yield/Worst-Case Distance - Linear Performance. 5.4 General Worst-Case Analysis. 5.5 Yield/Worst-Case Distance - Nonlinear Performance. 5.6 Exercise. 6. Yield Analysis. 6.1 Statistical Yield Analysis. 6.2 Tolerance Classes. 6.3 Geometric Yield Analysis. 6.4 Exercise. 7. Yield Optimization/Design Centering. 7.1 Statistical-Yield Optimization. 7.2 Geometric-Yield Optimization. A Expectation Values. A.1 Expectation Value. A.2 Moments. A.3 Mean Value. A.4 Central Moments. A.5 Variance. A.6 Covariance. A.7 Correlation. A.8 Variance/Covariance Matrix. A.9 Calculation Formulas.A.10 Standardization of Random Variables. A.11 Exercises. B Statistical Estimation of Expectation Values. B.1 Expectation-Value Estimator. B.2 Variance Estimator. B.3 Estimator Bias. B.4 Estimator Variance. B.5 Expectation-Value-Estimator Variance. B.6 Estimator Calculation Formulas. B.7 Exercises. C Optimality Conditions of Nonlinear Optimization Problems. C.1 Unconstrained Optimization. C.2 First-Order Unconstrained Optimality Condition. C.3 Second-Order Unconstrained Optimality Condition. C.4 Constrained Optimization. C.5 First-Order Constrained Optimality Condition. C.6 Second-Order Constrained Optimality Condition. C.7 Bounding-Box-of-Ellipsoids Property.