Core Insight：
Cross-Domain Data Collaboration: Unlocking Chip Potential
Eliminating the "efficiency gap" caused by knowledge silos is the starting point for industry potential. Data doesn't lie; Data Science provides the solution: Through innovation and cross-domain restructuring, we visualize results to become the critical accelerator for boosting efficiency.

GenAI Reshaping the Future of Semiconductors
Facing the immense challenge of the slowing pace of Moore's Law, the semiconductor industry urgently requires new breakthroughs. This book is specifically designed to solve the "Efficiency Black Hole" that consumes tens of billions of dollars annually in the industry.
The Empowering DTCO Innovation with AI and Machine Learning offers readers a practical DTCO.ML Framework, demonstrating how to leverage Machine Learning (ML) and Generative AI (GenAI) technologies to inject new acceleration into chip manufacturing processes. Learn to master process variability and optimize chip energy efficiency, eliminating the time-consuming and costly physical tape-out trial-and-error cycle.
You Will Master: How to use data to transform Yield improvement from relying on lengthy trial-and-error into a predictable, controllable process with Accelerated ROI; achieving significant Energy Efficiency (EE) leaps in every product iteration; and gaining a Time-to-Market (TTM) competitive advantage of several months for your team.
Whether you are a chip design engineer, process R&D expert, or a manager seeking industry "re-acceleration" strategies, this book provides a validated AI-enabled strategy and execution blueprint. The future of DTCO starts here.

Hock Chen, Ph.D. is the CEO of DigWise Technology and a pioneer in applying Machine Learning and GenAI to semiconductor process optimization. Dr. Chen previously served as CEO of DipSci Technology, where he focused on utilizing ML for chip process monitoring and production efficiency enhancement. His current research focuses on the Design-Technology Co-Optimization (DTCO) of chips, low-power design, and process optimization through GenAI. He holds a Ph.D. in Computer Science from National Tsing Hua University (Hsinchu, Taiwan, 2012).

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