GPMS TRANSPORTAL APPLIED AI COURSES

This course explores the use of advanced AI techniques, such as reinforcement learning and deep neural networks, to design next-generation semiconductor      devices with an emphasis on performance and energy efficiency.

Level: Advanced

Prerequisites: Strong foundation in semiconductor principles and deep learning techniques.

Assessments: Micro Assessment weekly, Full Assessment in the final week.

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Week 1 - Introduction to Advanced AI Techniques in Semiconductor Design

Learning Outcome

Understand the advanced AI techniques used in next-generation semiconductor design

1.1 Reinforcement learning in device design.
1.2 Deep neural networks in semiconductor modeling.
1.3 Overview of next-generation semiconductor devices.

Practical Component
Introduction to reinforcement learning for chip design. Students will create a basic reinforcement learning model for optimizing semiconductor devices.

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Week 2 - AI-Driven Energy Efficiency Optimization

Learning Outcome

Learn how AI optimizes energy consumption in semiconductor devices for low-power applications.

2.1 AI models for energy optimization.
2.2 Deep learning techniques for power efficiency in chips.
2.3 Balancing performance and energy efficiency using AI.

Practical Component
Implement energy optimization algorithms using AI techniques for semiconductor devices.

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Week 3 - AI for Advanced Circuit Simulation and Modeling

Learning Outcome

Understand how AI enhances circuit simulations and provides more accurate modeling for complex semiconductor devices.

3.1 AI techniques for improving circuit simulation accuracy.
3.2 Neural networks in predictive modeling.
3.3 AI-based parameter tuning for complex circuits.

Practical Componenet
Students will use AI models to simulate and optimize advanced semiconductor circuits.

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Week 4 - AI for High-Speed Semiconductor Design

Learning Outcome

Learn how AI helps in designing high-speed, high-frequency semiconductor devices.

4.1 High-speed circuit design with AI.
4.2 AI for signal integrity and optimization.
4.3 AI tools for designing ultra-fast semiconductor devices.

Practical Componenet
Practical exercise on designing high-speed circuits using AI tools.

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Week 5 - AI-Enhanced Packaging and Integration Techniques

Learning Outcome

Learn how AI improves packaging and integration of semiconductor devices for improved performance and miniaturization.

5.1 AI models for packaging optimization.
5.2 AI-driven integration of multi-chip systems.

5.3 Using AI to solve integration challenges for advanced semiconductor packaging.
Practical Component
Students will apply AI tools to optimize semiconductor packaging designs.

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Week 6 - Capstone Project and Assessment

Learning Outcome

Integrate advanced AI techniques into the design and optimization of a next-generation semiconductor device.

6.1 Full design flow using AI for performance and energy optimization.
6.2 Self-assessment and peer review of final projects.
6.3 Final presentation of optimized semiconductor device designs.

Practical Component
Students will complete a full design and optimization project, incorporating AI techniques in chip design, performance enhancement, and energy efficiency.

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