GPMS TRANSPORTAL APPLIED AI COURSES

This course introduces how AI can be used for reliability testing, failure analysis, and lifetime prediction of semiconductor devices. Students will learn advanced AI      techniques for failure mode detection and corrective actions, ultimately helping to extend the life of semiconductor products.

Level: Advanced

Prerequisites: Advanced understanding of semiconductor devices and failure mechanisms.

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

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Week 1 - Overview of Semiconductor Reliability Testing

Learning Outcome

Students will learn the basics of semiconductor reliability testing and how AI can be used to automate and improve these processes.

1.1 Introduction to reliability testing of semiconductors.
1.2 Common failure modes and their causes.
1.3 Role of AI in automating reliability testing.

Practical Component
Set up AI models to simulate the reliability testing of semiconductor devices.

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Week 2 - AI for Accelerated Stress Testing

Learning Outcome

Understand how AI can accelerate stress testing and simulate device behavior under extreme conditions.

2.1 AI for accelerated stress testing and lifecycle analysis.
2.2 Predicting failure modes under high stress conditions.
2.3 Using AI for thermal and voltage stress testing.

Practical Component
Students will simulate accelerated stress tests using AI models and analyze the results.

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Week 3 - Failure Mode Detection Using AI

Learning Outcome

Learn how AI algorithms detect failure modes early in the design or manufacturing process.

3.1 AI-based failure mode detection techniques.
3.2 Machine learning for predictive failure analysis.
3.3 Identifying early signs of failure in semiconductor components.

Practical Componenet
Use AI tools to identify failure modes in simulated semiconductor components.

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Week 4 - Predicting Device Lifetime with AI

Learning Outcome

Learn how AI can predict the remaining lifetime of semiconductor devices.

4.1 Machine learning for predicting the remaining useful life of devices.
4.2 AI-based wear-out modeling.
4.3 Predictive maintenance using AI.

Practical Componenet
Implement predictive maintenance models and lifetime prediction for semiconductor devices.

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Week 5 - AI for Root Cause Analysis of Failures

Learning Outcome

Understand how AI helps in determining the root cause of failures in semiconductor devices and processes.

5.1 Root cause analysis using machine learning.
5.2 AI for corrective action and process improvements. .
5.3 Automation of fault isolation using AI.

Practical Component
Students will use AI to identify and fix faults in semiconductor devices.

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Week 6 - Capstone Project and Assessment

Learning Outcome

Complete a comprehensive project involving AI-based reliability testing, failure analysis, and lifetime prediction for semiconductor devices.

6.1 Full reliability and failure analysis cycle using AI
6.2 Peer assessment and final project review.
6.3 Final presentation and project feedback.

Practical Component
Students will present their findings and improvements from the failure analysis and lifetime prediction project using AI.

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