This course dives deeper into the application of AI in the semiconductor manufacturing process, focusing on predictive maintenance, process optimization, defect detection, and yield improvement.
Level - Intermediate
Prerequisites - Knowledge of basic semiconductor principles and some exposure to machine learning.
Duration - 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments - Micro Assessment weekly, Full Assessment in the final week.
This course covers the application of AI to streamline semiconductor supply chain management and optimize the semiconductor manufacturing process. Students will learn how AI can predict supply chain issues, improve production scheduling, and reduce costs in semiconductor manufacturing.
Level- Intermediate
Prerequisites - Basic understanding of supply chain management and manufacturing processes.
Duration- 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments - Micro Assessment weekly, Full Assessment in the final week.
This course introduces how AI can enhance chip design, focusing on automation in layout planning, circuit optimization, and predicting chip behavior. It covers fundamental AI principles applied to semiconductors and practical tools for chip design using AI technologies.
Level- Mandatory - Introduction
Prerequisites- Basic understanding of electronics and semiconductors. No coding experience needed; will be built during the course.
Duration- 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments - Micro Assessment weekly, Full Assessment in the final week.
This course dives deeper into the application of AI in the semiconductor manufacturing process, focusing on predictive maintenance, process optimization, defect detection, and yield improvement.
Level -Intermediate
Prerequisites - Knowledge of basic semiconductor principles and some exposure to machine learning.
Duration - 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments -Micro Assessment weekly, Full Assessment in the final week.
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.
Duration- 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments-Micro Assessment weekly, Full Assessment in the final week.
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.
Duration- 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments - Micro Assessment weekly, Full Assessment in the final week./p>
This course covers the application of AI to streamline semiconductor supply chain management and optimize the semiconductor manufacturing process. Students will learn how AI can predict supply chain issues, improve production scheduling, and reduce costs in semiconductor manufacturing.
Level -Intermediate
Prerequisites-Basic understanding of supply chain management and manufacturing processes.
Duration- 36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments - Micro Assessment weekly, Full Assessment in the final week.
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.
Duration-36 Hours (6 Hours per week - 2 Hrs x 3)
Assessments-Micro Assessment weekly, Full Assessment in the final week.
