GPMS TRANSPORTAL APPLIED AI COURSES

This course focuses on using AI to characterize semiconductor devices, analyze their performance, and optimize their behavior for various applications such as      mobile, automotive, and IoT. Students will learn how AI models can predict and improve semiconductor device characteristics like speed, power, and reliability.

Level: Intermediate

Prerequisites: Understanding of semiconductor device physics and basic machine learning concepts.

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

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Week 1 - Introduction to Semiconductor Device Characterization

Learning Outcome

Students will understand the fundamentals of semiconductor device characterization, including key parameters such as threshold voltage, leakage current, and mobility.

1.1 Overview of semiconductor devices and their behavior.
1.2 Key parameters in device characterization.
1.3 Challenges in accurately characterizing semiconductor devices.

Practical Component
Students will analyze and characterize a simple semiconductor device using basic measurement tools and AI-based analysis methods.

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Week 2 - Using AI for Predictive Performance Modeling

Learning Outcome

Learn how AI can predict semiconductor device performance under varying conditions.

2.1 Overview of performance parameters: speed, power, reliability.
2.2 Machine learning models for predictive performance analysis.
2.3 Using AI to predict device performance across temperature and voltage variations.

Practical Component
Implement AI models to predict the performance of a semiconductor device based on input parameters

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Week 3 - Optimization Algorithms for Device Tuning with AI

Learning Outcome

Students will understand how AI is used to optimize semiconductor device performance in real-time.

3.1 Using AI for power and performance tuning.
3.2 AI algorithms for temperature compensation.
3.3 Real-time optimization using reinforcement learning.

Practical Componenet
Apply optimization techniques to tune the power-performance trade-off of a semiconductor device.

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Week 4 - AI for Reliability Testing and Stress Simulation

Learning Outcome

Learn how AI can be used to simulate stress testing and improve the reliability of semiconductor devices.

4.1 AI for accelerated aging and stress testing of devices.
4.2 Predicting failure modes and life expectancy of devices.
4.3 Machine learning for failure analysis in semiconductor devices.

Practical Componenet
Use AI tools to run simulations that predict the lifespan and failure points of semiconductor devices.

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Week 5 - AI-Driven Fault Detection and Self-Healing in Semiconductor Devices

Learning Outcome

Understand how AI helps detect faults in semiconductor devices and implements self-healing mechanisms.

5.1 Fault detection techniques using AI and machine learning.
5.2 AI-based self-healing mechanisms for semiconductor devices.
5.3 Detecting and correcting faults in real-time.

Practical Component
Implement a machine learning model that detects faults and triggers self-healing mechanisms in semiconductor devices.

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Week 6 - Capstone Project and Assessment

Learning Outcome

Students will complete a project involving the characterization, performance tuning, and optimization of semiconductor devices using AI tools.

6.1 Characterization, optimization, and performance prediction of a semiconductor device using AI.
6.2 Peer assessments and final project review.
6.3 Final presentations and feedback.

Practical Component
Students will design a complete process to characterize, predict performance, and optimize a semiconductor device using AI.

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