Table of Contents



Foreword

AI-Driven Semiconductor Chip Design Efficiency and Productivity Revolution



Part I: Design Technology Co-Optimization, DTCO

Chapter 1 Overview and Evolution of DTCO

1.1 Principles of Design for Productivity

1.2 Design Methodology for Ultimate Efficiency

1.3 Future Directions of DTCO



Chapter 2 Key Challenges and Strategies in Driving DTCO

2.1 Key Challenges in Implementing DTCO

2.2 Demand for Innovative Design Methods

2.3 Productivity Optimization Platform Development



Chapter 3 Optimizing Chip Energy Efficiency and Productivity

3.1 Preparatory Work Before Project Initiation

3.2 Custom Cell and Timing Signoff Strategy

3.3 Process Optimization and Analysis Techniques

3.4 Compensation Mechanism Design and Implementation

3.5 Challenges and Demands of Near-Threshold Voltage Technology



Part II: DTCO.ML ? : Machine Learning-Driven Semiconductor Process Optimization

Chapter 4 The Integration of Machine Learning and DTCO (DTCO.ML ? )

4.1 Virtual Wafer Data Modeling (Virtual Silicon)

4.2 Building and Inferring Regression Models

4.3 Application of Data Tracking and Production Optimization



Chapter 5 Library Metric Extraction and Analysis System (libMetric ? )

5.1 Cell Timing and Power Modeling

5.2 Cell Feature Extraction

5.3 RO Simulation

5.4 Standard Cell Library Batch PPA Benchmarking



Chapter 6 On-Chip Sensor Design and Integration (GRO Compiler)

6.1 Goal-Oriented RO Design

6.2 SPICE-to-Silicon Correlation

6.3 Process Monitoring and Optimization

6.4 On-Chip Effective Voltage Analysis

6.4.1 Local Voltage Distribution Monitoring

6.4.2 Compensation Strategy

6.4.3 Dynamic Timing Slack Alerts and Layout

6.5 GRO Automation Tool and Verification Process



Chapter 7 Data Analysis and Machine Learning Platform (Copernic ? )

7.1 Data Standardization and Visualization

7.2 Cross-Domain Mapping of Multi-Dimensional Data

7.3 Design Flow Integration Strategy

7.3.1 WAT-aware Timing Re-K

7.3.2 WAT-CP Mapping and Correlation Analysis

7.3.3 OCV Analysis

7.4 OCV Analysis and Design Margin Optimization

7.5 Post-Silicon Analysis and Optimization



Chapter 8 Chip Performance Rating Strategy and Optimization (Binning-PG ? )

8.1 Impact of Binning Strategy on Productivity

8.2 Chip Characteristics Analysis and Challenges

8.3 Binning Policy Generation (Binning-PG ? )

8.4 Automated Policy Generation and Optimization

8.5 On-chip Self-binning Application



Part III: DTCO.GenAI ? : Generative AI-Driven Chip Design Innovation

Chapter 9 Generative AI and DTCO Integration (DTCO.GenAI ? )

9.1 Limitations of Traditional Modeling Methods

9.2 Following the Trail: Multivariate Normal Distribution

9.3 Virtual Silicon Data in DTCO (DTCO.VS)



Chapter 10 Virtual Silicon Data Generation Technology (DTCO.VS)

10.1 Dataset Preparation

10.2 GAN-based Virtual Silicon (GAN-VS)

10.2.1 GAN Model

10.2.2 GAN Model Performance Evaluation

10.3 Diffusion Model-based Virtual Silicon (DM-VS)

10.3.1 Denoising Diffusion Probabilistic Model (DDPM))

10.3.2 Diffusion Model Performance Evaluation



Chapter 11 Generative AI-Driven Chip Efficiency Optimization and Modeling

11.1 WAT Super Resolution (WAT-SR)

11.2 High-Efficiency SPICE-Silicon Bias Modeling (He-SSBM)

11.2.1 Design Principle of One-shot SPICE-Silicon N/P Correlation

11.2.2 Design and Signoff Strategy Optimization

11.3 High-Fidelity Generative Monte Approximation (HΣ- GMA)

11.3.1 Limitations of Traditional Monte Carlo Methods

11.3.2 Innovative Application of Generative Neural Networks



Chapter 12 Conclusion and Outlook

12.1 AI-Enhanced DTCO: Revolutionizing Chip Design and Process Optimization (DTCO.ML ? )

12.2 Generative AI-Driven Optimization (DTCO.GenAI ? )

12.3 EDA Innovation and Future Outlook



Appendix

Open Source Resource List

Reference List

Glossary of Terms?

Preface

In the era of AI-driven innovation, the semiconductor industry is shifting from traditional yield optimization to comprehensive productivity enhancement. To stay competitive, chip design now focuses on enhancing performance, production capacity, and market competitiveness, beyond just yield and cost. This book explores how AI can optimize design margins, timing signoff, testing strategies, data analysis, process adjustments, binning strategies, and system-level compensation, driving a revolution in semiconductor design and productivity.



Book Positioning

This book approaches semiconductor design from a physical implementation perspective, focusing on the application of machine learning (ML) in Design-Technology Co-Optimization (DTCO). It covers traditional ML and cuttingedge generative AI (GenAI), exploring strategies to enhance chip performance and productivity.



Target Audience

Semiconductor Professionals: Chip design engineers, EDA developers, and researchers, offering practical examples and insights.

Cross-Disciplinary Researchers: Helping them understand DTCO and explore AI applications.



Book Structure

We introduce DTCO principles, discuss machine learning applications in optimization, and analyze how generative AI shapes the future of semiconductor design. The book also explores innovative EDA development, showing how new technologies improve design efficiency and performance.