Advanced AI Techniques for Semiconductor Design Automation

• Experience in semiconductor design and EDA tools
• Advanced knowledge of AI and machine learning techniques
• Familiarity with neural networks

Audience

• Semiconductor design engineers
• AI specialists in semiconductor industries
• EDA tool developers

AI is increasingly integral to the design and verification of semiconductor chips, with advanced techniques such as machine learning and neural networks enhancing the capabilities of Electronic Design Automation (EDA) tools.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to apply cutting-edge AI techniques to semiconductor design automation, improving efficiency, accuracy, and innovation in chip design and verification.

By the end of this training, participants will be able to:

• Apply advanced AI techniques to optimize semiconductor design processes.
• Integrate machine learning models into EDA tools for enhanced design verification.
• Develop AI-driven solutions for complex design challenges in chip fabrication.
• Leverage neural networks for improving the accuracy and speed of design automation.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to AI in Semiconductor Design Automation

• Overview of AI applications in EDA tools
• Challenges and opportunities in AI-driven design automation
• Case studies of successful AI integration in semiconductor design

Machine Learning for Design Optimization

• Introduction to machine learning techniques for design optimization
• Feature selection and model training for EDA tools
• Practical applications in design rule checking and layout optimization

Neural Networks in Chip Verification

• Understanding neural networks and their role in chip verification
• Implementing neural networks for error detection and correction
• Case studies on the use of neural networks in EDA tools

Advanced AI Techniques for Power and Performance Optimization

• Exploring AI techniques for power and performance analysis
• Integrating AI models to optimize power efficiency
• Real-world examples of AI-driven performance enhancement

EDA Tool Customization with AI

• Customizing EDA tools with AI for specific design challenges
• Developing AI plugins and modules for existing EDA platforms
• Hands-on practice with popular EDA tools and AI integration

Future Trends in AI for Semiconductor Design

• Emerging AI technologies in semiconductor design automation
• Future directions in AI-driven EDA tools
• Preparing for advancements in AI and semiconductor industries

Summary and Next Steps