
Nano-Scale CMOS Analog Circuits : Models and CAD Techniques for High-Level Design
[BOOK DESCRIPTION]
Reliability concerns and the limitations of process technology can sometimes restrict the innovation process involved in designing nano-scale analog circuits. The success of nano-scale analog circuit design requires repeat experimentation, correct analysis of the device physics, process technology, and adequate use of the knowledge database. Starting with the basics, Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design introduces the essential fundamental concepts for designing analog circuits with optimal performances. This book explains the links between the physics and technology of scaled MOS transistors and the design and simulation of nano-scale analog circuits. It also explores the development of structured computer-aided design (CAD) techniques for architecture-level and circuit-level design of analog circuits. The book outlines the general trends of technology scaling with respect to device geometry, process parameters, and supply voltage. It describes models and optimization techniques, as well as the compact modeling of scaled MOS transistors for VLSI circuit simulation.* Includes two learning-based methods: the artificial neural network (ANN) and the least-squares support vector machine (LS-SVM) method * Provides case studies demonstrating the practical use of these two methods * Explores circuit sizing and specification translation tasks * Introduces the particle swarm optimization technique and provides examples of sizing analog circuits * Discusses the advanced effects of scaled MOS transistors like narrow width effects, and vertical and lateral channel engineering Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design describes the models and CAD techniques, explores the physics of MOS transistors, and considers the design challenges involving statistical variations of process technology parameters and reliability constraints related to circuit design.
[TABLE OF CONTENTS]
List of Figures xv
List of Tables xxi
Preface xxiii
About the Authors xxvii
1 Introduction 1 (34)
1.1 Introduction 1 (1)
1.2 Characterization of Technology Scaling 2 (12)
1.2.1 Constant Field Scaling 2 (1)
1.2.2 Constant Voltage Scaling 2 (2)
1.2.3 Nonscaling Effects 4 (1)
1.2.4 Generalized Scaling and Technology 5 (9)
Trends
1.2.4.1 International Technology Roadmap 5 (1)
for Semi-conductors
1.2.4.2 Predictive Technology Modeling 6 (1)
1.2.4.3 Scaling of Geometry Parameters 6 (2)
1.2.4.4 Scaling of Channel Doping, Supply 8 (2)
Voltage, and Threshold Voltage
1.2.4.5 Scaling of Performances 10 (2)
1.2.4.6 Scaling of Source-Drain 12 (2)
Resistance and Saturation Velocity
1.3 Analog Design Challenges in Scaled CMOS 14 (5)
Technology
1.3.1 Degradation of Output Resistance and 14 (1)
Intrinsic Gain
1.3.2 Gate Oxide Leakage Current 15 (1)
1.3.3 Noise Performance 16 (1)
1.3.4 Analog Power Consumption 16 (2)
1.3.5 Drain Current Mismatch 18 (1)
1.3.6 Transition Frequency 18 (1)
1.3.7 Reliability Constraints 18 (1)
1.4 Motivation for CAD Techniques 19 (3)
1.4.1 Design Productivity Gap 19 (1)
1.4.2 Design Creativity Gap 20 (2)
1.5 Conventional Design Techniques for Analog 22 (6)
IC Design
1.5.1 Bottom-Up Design Technique 22 (2)
1.5.1.1 Advantages and Limitations 24 (1)
1.5.2 Top-Down Design Technique 24 (4)
1.5.2.1 Abstraction Levels 25 (1)
1.5.2.2 Hierarchical Design Strategy 26 (1)
1.5.2.3 Advantages and Limitations 26 (2)
1.6 Knowledge-Based CAD Technique for Analog 28 (5)
ICs
1.6.1 Motivation for Knowledge Extraction 29 (1)
and Management
1.6.2 Problem Formulations 29 (1)
1.6.3 Outline of the Procedure 30 (2)
1.6.3.1 Numerical Simulation-Based 30 (2)
Evaluation
1.6.3.2 Analytical Model-Based Evaluation 32 (1)
1.6.4 Salient Features 32 (1)
1.7 Summary and Conclusion 33 (2)
2 High-Level Modeling and Design Techniques 35 (50)
2.1 Introduction 35 (1)
2.2 High-Level Model 36 (3)
2.2.1 Behavioral Models 36 (1)
2.2.2 Performance Models 37 (1)
2.2.3 Feasibility Models 37 (1)
2.2.4 Characteristics of Good High-Level 38 (1)
Models
2.3 Behavioral Model Generation Technique 39 (10)
2.3.1 Manual Abstraction 39 (2)
2.3.2 Model Order Reduction Technique 41 (3)
2.3.2.1 MOR, for LTI Systems 41 (2)
2.3.2.2 MOR, for LTV Systems 43 (1)
2.3.2.3 MOR, for Nonlinear Systems 43 (1)
2.3.3 Symbolic Analysis Technique 44 (5)
2.3.3.1 Basic Concepts 44 (2)
2.3.3.2 Methodology 46 (1)
2.3.3.3 Simplification of Expressions 47 (2)
2.4 Introduction to Optimization Techniques 49 (7)
2.4.1 Optimization Problem Formulation 49 (2)
2.4.2 Optimality Criteria 51 (1)
2.4.3 Classification of Optimization 51 (3)
Algorithms
2.4.3.1 Single-Variable Optimization 51 (1)
Algorithms
2.4.3.2 Multi-Variable Optimization 51 (1)
Algorithm
2.4.3.3 Constrained Optimization 52 (1)
Algorithms
2.4.3.4 Specialized Optimization 53 (1)
Algorithms
2.4.3.5 Nontraditional Stochastic 53 (1)
Optimization Algorithms
2.4.4 Concept of Local Optima and Global 54 (1)
Optima
2.4.5 Characterization of Optimization 55 (1)
Algorithms
2.5 Some Important Optimization Algorithms 56 (5)
2.5.1 Cauchy's and Newton's Steepest 56 (1)
Descent Algorithm
2.5.2 Genetic Algorithm 57 (3)
2.5.3 Simulated Annealing 60 (1)
2.6 Multi-Objective Optimization Method 61 (2)
2.6.1 Pareto Optimal Front 62 (1)
2.7 Design Space Exploration 63 (1)
2.8 Computational Complexity of a CAD 63 (5)
Algorithm
2.8.1 Time and Space Complexity 64 (1)
2.8.2 Asymptotic Notations 65 (2)
2.8.2.1 Big-Oh Notation O() 65 (1)
2.8.2.2 Omega Notation Ω() 66 (1)
2.8.2.3 Theta Notation Θ() 66 (1)
2.8.3 Categorization of CAD Problems 67 (1)
2.8.4 Complexity Classes for CAD Problems 67 (1)
2.9 Technology-Aware Computer Aided IC Design 68 (7)
Technique
2.9.1 Introduction to TCAD 69 (1)
2.9.2 Process Simulation through TCAD 70 (1)
2.9.3 Device Simulation through TCAD 70 (1)
2.9.4 Design for Manufacturability and Yield 71 (2)
2.9.5 Process Compact Model Development 73 (2)
through TCAD
2.9.5.1 Parameter Extraction Technique 74 (1)
2.9.6 Design Techniques for Nano-Scale 75 (1)
Analog ICs
2.10 Commercial Design Tools 75 (9)
2.10.1 IC Design 75 (6)
2.10.1.1 Cadenceョ Virtuoso Analog Design 77 (2)
Environment
2.10.1.2 Synopsys Galaxy Custom Design 79 (1)
2.10.1.3 Tanner EDA HiPer Siliconョ 80 (1)
2.10.1.4 Mentor Graphics Pyxisョ Suite 81 (1)
2.10.2 TCAD 81 (5)
2.10.2.1 Silvaco Tool Suite 81 (2)
2.10.2.2 Synopsys Device Simulation Tool 83 (1)
Suite
2.11 Summary and Conclusion 84 (1)
3 Modeling of Scaled MOS Transistor for VLSI 85 (72)
Circuit Simulation
3.1 Introduction 85 (1)
3.2 Device Modeling 86 (1)
3.2.1 Categories of Device Models 87 (1)
3.3 Compact Models 87 (2)
3.3.1 Commercial Compact Models 88 (1)
3.4 Long-Channel MOS Transistor 89 (1)
3.5 Threshold Voltage Model for Long-Channel 90 (2)
Transistor with Uniform Doping
3.5.1 Body Effect 91 (1)
3.6 SPICE Level 1 Drain Current Model 92 (5)
3.6.1 Channel Length Modulation Effect 96 (1)
3.7 SPICE Level 3 I-V Model 97 (2)
3.8 MOSFET Capacitances 99 (11)
3.8.1 Characterization of Intrinsic 100 (4)
Capacitances
3.8.1.1 Charge Partitioning 103 (1)
3.8.2 Characterization of Extrinsic 104 (3)
Capacitances
3.8.2.1 Overlap and Fringing Capacitances 104 (1)
3.8.2.2 Junction Capacitances 105 (2)
3.9 Short-Channel MOS Transistor 107 (3)
3.10 Threshold Voltage for Short-Channel MOS 110 (10)
Transistor
3.10.1 Source/Drain Charge Sharing 110 (2)
3.10.1.1 Level 2 Compact Model for VT 111 (1)
3.10.2 Drain-Induced Barrier Lowering 112 (2)
3.10.2.1 Level 3 Model of DIBL 113 (1)
3.10.3 BSIM3/BSIM4 Compact Model for 114 (3)
Threshold Voltage
3.10.4 Short-Channel Effect Immunity 117 (3)
3.11 I-V Model for Short-Channel MOS 120 (8)
Transistor
3.11.1 Carrier Mobility Degradation 120 (3)
3.11.1.1 Surface Mobility 120 (1)
3.11.1.2 Mobility Dependence on Gate Field 121 (2)
3.11.2 Carrier Velocity Saturation 123 (1)
3.11.3 Drain Current in Scaled MOS 124 (4)
Transistor
3.12 Weak Inversion Characteristics of a 128 (6)
Scaled MOS Transistor
3.12.1 Subthreshold Swing 131 (3)
3.13 Hot Carrier Effect 134 (7)
3.13.1 Spatial Distribution of Lateral 134 (3)
Electric Field
3.13.2 Substrate Current Due to Hot-Carrier 137 (2)
Effects
3.13.3 Gate Current Due to Hot-Carrier 139 (2)
Effects: Lucky Electron Model
3.13.4 Reduction of Drain Field through LDD 141 (1)
Structure
3.14 Source-Drain Resistance Model 141 (4)
3.14.1 Compact Modeling 143 (1)
3.14.2 Salicide Technology 144 (1)
3.15 Physical Model for Output Resistance 145 (5)
3.15.1 Compact Modeling 147 (3)
3.16 Poly-Silicon Gate Depletion Effect 150 (2)
3.16.1 Electrical Oxide Thickness 152 (1)
3.16.2 Reduction of Poly-Gate Depletion 152 (1)
3.17 Effective Channel Length and Width 152 (4)
3.17.1 Effective Channel Length 154 (6)
3.17.1.1 Extraction of the Effective 155 (1)
Channel Length
3.18 Summary and Conclusion 156 (1)
4 Performance and Feasibility Model Generation 157 (60)
Using Learning-Based Approach
4.1 Introduction 157 (1)
4.2 Requirement of Learning-Based Approaches 157 (1)
4.3 Regression Problem for Performance Model 158 (1)
Generation
4.4 Some Related Works 158 (2)
4.5 Preliminaries on Artificial Neural Network 160 (4)
4.5.1 Basic Components 160 (1)
4.5.2 Mathematical Model of Neuron 160 (1)
4.5.3 MLP Feed-Forward NN Structure 161 (1)
4.5.4 Feed-Forward Computation 162 (1)
4.5.5 Success of MLP NN Structures 163 (1)
4.5.6 Network Size and Layers 164 (1)
4.6 Neural Network Model Development 164 (7)
4.6.1 Formulation of Inputs and Outputs 164 (1)
4.6.2 Data Range and Sample Distribution 164 (3)
4.6.3 Data Collection 167 (1)
4.6.4 Data Organization and Data 167 (1)
Preprocessing
4.6.5 Neural Network Training 168 (2)
4.6.6 Quality Measures 170 (1)
4.6.7 Generalization Ability, Overlearning, 170 (1)
and Underlearning
4.7 Case Study 1: Performance Modeling of 171 (3)
CMOS Inverter
4.8 Case Study 2: Performance Modeling of 174 (8)
Spiral Inductor
4.9 Dynamic Adaptive Sampling 182 (5)
4.9.1 Motivation of the Algorithm 183 (1)
4.9.2 Simple Dynamic Sampling Algorithms 183 (1)
4.9.3 Dynamic Adaptive Sampling Algorithm 184 (2)
4.9.3.1 Initial Sample Size 184 (1)
4.9.3.2 Sampling Schedule 184 (1)
4.9.3.3 Stopping Criteria 185 (1)
4.9.4 Demonstration with CMOS Inverter 186 (1)
Problem
4.10 Introduction to Least Squares Support 187 (11)
Vector Machines
4.10.1 Least-Squares Support Vector 187 (2)
Regression
4.10.2 Least-Squares Support Vector 189 (3)
Classification
4.10.2.1 Classifier Accuracy 191 (1)
4.10.3 Choice of Kernel Functions and 192 (9)
Hyperparameter Tuning
4.10.3.1 Grid Search Technique 194 (2)
4.10.3.2 Genetic Algorithm-Based Technique 196 (2)
4.11 Feasible Design Space and Feasibility 198 (3)
Model
4.12 Case Study 3: Combined Feasibility and 201 (3)
Performance Modeling of Two-Stage Operational
Amplifier
4.12.1 Feasibility Model 202 (1)
4.12.2 Performance Model 203 (1)
4.13 Case Study 4: Architecture-Level 204 (4)
Performance Modeling of Analog Systems
4.14 Meet-in-the-Middle Approach for 208 (3)
Construction of Architecture-Level Feasible
Design Space
4.14.1 Application Bounded Space Da 208 (2)
4.14.2 Circuit Realizable Space Dc 210 (1)
4.14.3 Feasible Design Space Identification 210 (1)
4.15 Case Study 5: Construction of 211 (4)
Feasibility Model at Architecture Level of an
Interface Electronics for MEMS Capacitive
Accelerometer System
4.16 Summary and Conclusion 215 (2)
5 Circuit Sizing and Specification Translation 217 (36)
5.1 Introduction 217 (1)
5.2 Circuit Sizing as a Design Space 217 (4)
Exploration Problem
5.2.1 Problem Formulations 217 (2)
5.2.2 Solution Techniques 219 (1)
5.2.3 Design Flow 220 (1)
5.2.3.1 Evaluation of Cost Functions 221 (1)
5.3 Particle Swarm Optimization Algorithm 221 (5)
(PSO)
5.3.1 Dynamics of a Particle in PSO 222 (1)
5.3.2 Flow of the Algorithm 223 (1)
5.3.3 Selection of Parameters for PSO 223 (14)
5.3.3.1 Inertia Weight ω 225 (1)
5.3.3.2 Maximum Velocity Vmax 225 (1)
5.3.3.3 Swarm Size S 225 (1)
5.3.3.4 Acceleration Coefficient c1 and c2 225 (1)
5.4 Case Study 1: Design of a Two-Stage 226 (3)
Miller OTA
5.5 Case Study 2: Synthesis of on-Chip Spiral 229 (5)
Inductors
5.6 Case Study 3: Design of a Nano-Scale CMOS 234 (3)
Inverter for Symmetric Switching
Characteristics
5.7 The gm/ID Methodology for Low Power Design 237 (13)
5.7.1 Study of the gm/ID and サ Parameters 238 (3)
for Analog Design
5.7.2 gm/ID Based Sizing Methodology 241 (5)
5.7.3 Case Study 4: Sizing of Low-Power 246 (4)
Nano-Scale Miller OTA Using the gm/ID
Methodology
5.8 High-Level Specification Translation 250 (1)
5.9 Summary and Conclusion 251 (2)
6 Advanced Effects of Scaled MOS Transistors 253 (52)
6.1 Introduction 253 (1)
6.2 Narrow Width Effect on Threshold Voltage 253 (4)
6.2.1 LOCOS Isolated MOS Transistors 254 (1)
6.2.2 Shallow Trench Isolated (STI) MOSFETs 255 (2)
6.3 Channel Engineering of MOS Transistor 257 (6)
6.3.1 Non-Uniform Vertical Doping 257 (4)
6.3.1.1 High-to-Low Profile 258 (2)
6.3.1.2 Low-to-High Retrograde Profile 260 (1)
6.3.1.3 Compact Modeling of Vertical 260 (1)
Non-Uniform Doping Effect
6.3.2 Pocket (Halo) Implantation 261 (2)
6.4 Gate Leakage Current 263 (12)
6.4.1 Basic Ideas about Quantum Mechanical 263 (3)
Tunneling
6.4.2 Gate Oxide Tunneling Current 266 (3)
6.4.2.1 Energy Band Theory Model 266 (1)
6.4.2.2 Fowler-Nordheim Tunneling 266 (3)
6.4.2.3 Direct Tunneling 269 (1)
6.4.3 Gate Leakage Mechanisms and Leakage 269 (3)
Components for MOS Transistors
6.4.4 Compact Modeling 272 (1)
6.4.5 Effects of Gate Leakage 272 (3)
6.5 High-κ Dielectrics and 275 (4)
Metal-Gate/High-κ CMOS Technology
6.5.1 High-κ Dielectric Materials 275 (3)
6.5.2 Metal Gate 278 (1)
6.6 Advanced Device Structures of MOS 279 (5)
Transistors
6.6.1 SOI MOS Transistor 279 (1)
6.6.2 Double Gate (DG)-MOS Transistors 280 (2)
6.6.3 FinFETs 282 (2)
6.7 Noise Characterization of MOS Transistors 284 (10)
6.7.1 Fundamental Sources of Noise 284 (2)
6.7.2 Characterization of Thermal Noise in 286 (2)
MOS Transistors
6.7.3 Characterization of Flicker Noise in 288 (6)
MOS Transistors
6.7.3.1 Physical Mechanism of Flicker 288 (3)
Noise
6.7.3.2 Physics-Based Modeling of Flicker 291 (3)
Noise
6.8 Gate Resistance and Substrate Network 294 (8)
Model of MOS Transistor for RF Applications
6.8.1 Parasitic Components of MOS 297 (1)
Transistors
6.8.2 Gate Resistance Modeling 298 (3)
6.8.2.1 Minimization of Gate Resistance 300 (1)
6.8.3 Substrate Network Modeling 301 (1)
6.9 Summary and Conclusion 302 (3)
7 Process Variability and Reliability of 305 (46)
Nano-Scale CMOS Analog Circuits
7.1 Introduction 305 (1)
7.2 Basic Concepts on Yield and Reliability 306 (7)
7.2.1 Yield 306 (2)
7.2.2 Design Tolerance and Capability Index 308 (2)
7.2.3 Reliability Bathtub Curve 310 (3)
7.3 Sources of Variations in Nanometer Scale 313 (3)
Technology
7.3.1 Process Variations 314 (2)
7.3.2 Environmental Variations 316 (1)
7.3.3 Aging Variations or Reliability 316 (1)
7.4 Systematic Process Variations 316 (4)
7.4.1 Optical Proximity Correction 317 (1)
7.4.2 Phase Shift Mask 317 (1)
7.4.3 Layout-Induced Strain 318 (1)
7.4.4 Well Proximity Effect 319 (1)
7.5 Random Process Variations 320 (7)
7.5.1 Random Discrete Dopants 321 (1)
7.5.2 Line Edge Roughness 322 (5)
7.5.3 Oxide Thickness Variations 327 (1)
7.5.4 High-it Dielectric Morphology and 327 (1)
Metal Gate Granularity
7.6 Statistical Modeling 327 (8)
7.6.1 Worst Case Corner Analysis 329 (2)
7.6.2 Monte Carlo Simulation Technique 331 (1)
7.6.3 Statistical Corner Technique 331 (3)
7.6.4 Mismatch in Analog Circuits 334 (1)
7.7 Physical Phenomena Affecting the 335 (7)
Reliability of Scaled MOS Transistor
7.7.1 Time Dependent Dielectric Breakdown 337 (5)
(TDDB)
7.7.1.1 Electrostatics in Dielectrics 337 (1)
7.7.1.2 Energy Band Theory of Dielectric 338 (1)
Breakdown
7.7.1.3 Anode Hole Injection Theory 339 (2)
7.7.1.4 Percolation Theory 341 (1)
7.7.1.5 Statistics of Gate Oxide Breakdown 341 (1)
7.7.1.6 Soft and Hard Breakdown 341 (1)
7.7.2 Hot Carrier Injection (HCI) 342 (1)
7.7.3 Negative Bias Temperature Instability 342 (1)
(NBTI)
7.8 Physical Model for MOSFET Degradation Due 342 (5)
to HCI
7.8.1 Physical Mechanism for Interface Trap 343 (4)
Generation
7.8.1.1 Physical Model 344 (1)
7.8.1.2 Application to Analog Circuit 345 (2)
Design
7.9 Reaction-Diffusion Model for NBTI 347 (1)
7.10 Reliability Simulation for Analog 347 (2)
Circuits
7.11 Summary and Conclusion 349 (2)
Bibliography 351 (18)
Index 369