This module covers the basics of Artificial Intelligence, including key concepts such as machine learning, deep learning, neural networks, supervised and unsupervised learning, reinforcement learning and AI ethics. Students will gain a solid foundation in AI principles and methodologies.

Weeks 1-4 :- 

• Understanding what AI is and its different forms
• Basics of key AI algorithms
• Current state of AI applications
• Innovations driving the future of AI
• Case Studies

Development Tools :-

• Python Programming Environment (Jupyter Notebooks, Anaconda Distribution)
• Libraries and Frameworks (NumPy, Pandas, Scikit-learn)
• Integrated Development Environments (PyCharm, Visual Studio Code)
• Version Control (Git, GitHub)

Students will explore semiconductor physics, including doping, charge carriers, and P-N junctions. The module will cover Moore's Law, its historical significance, and its future implications for semiconductor technology. Key terminologies include crystal lattice, bandgap, and carrier mobility.

Weeks 5-8 :- 

• Semiconductor materials and their properties
• Crystal structures and electronic properties
• PN junctions and diodes
• Current applications and future trends
• Case Studies

Focusing on the importance of data in AI, this module covers data collection techniques, data cleaning and preprocessing. Topics include big data technologies, data augmentation, and feature engineering to enhance AI models. Students will learn about data normalization, scaling and transformation.

Weeks 9-12 :- 

• Role of data in semiconductor manufacturing
• Methods of data collection
• Importance of data cleaning
• Role of data annotation in supervised learning
• Case Studies

This module delves into AI algorithms and models relevant to the semiconductor industry. Students will study decision trees, support vector machines, neural networks and ensemble methods. The module also covers model evaluation metrics, hyperparameter tuning, and cross-validation.

Weeks 13-16 :-

• Types of AI algorithms used in semiconductor manufacturing
• Regression & classification algorithms
• Clustering & dimensionality reduction techniques
• Basics of reinforcement learning
• Case Studies

Students will learn how machine learning optimizes semiconductor manufacturing processes, such as lithography, etching  and deposition. The module includes predictive maintenance, anomaly detection and process control techniques. Key terms include process variation, yield prediction and fault detection.

Weeks 17-20 :-

• Importance of process optimization in semiconductor manufacturing
• Common ML techniques used in process optimization
• Concepts of advanced process control
• Importance of real-time optimization
• Case Studies

This module focuses on how AI enhances semiconductor design, specifically in Very-Large-Scale Integration (VLSI), System on Chip (SoC), and Field-Programmable Gate Array (FPGA) design. Students will learn about Electronic Design Automation (EDA) tools and simulation software, covering topics like logic synthesis, place and route and design for manufacturability (DFM). The integration of AI in these areas helps automate complex design tasks, optimize chip layouts and improve manufacturability, ultimately leading to more efficient and innovative semiconductor designs.

Weeks 21-24 :-

• Role of AI in Electronic Design Automation (EDA)

• Importance of circuit simulation in semiconductor design

• Challenges in design verification

• Importance of design optimization

• Case Studies

This module focuses on AI integration in semiconductor fabrication and production, covering Application-Specific Integrated Circuits (ASICs), wafer processing and yield enhancement. Students will explore real-time monitoring, control systems and defect detection methods. Key terms include photolithography, plasma etching and chemical vapor deposition (CVD). By understanding these processes and AI's role in optimizing them, students will learn how to improve production efficiency and product quality in semiconductor manufacturing.

Weeks 25-28 :- 

• Overview of semiconductor fabrication & production processes

• Importance of process control in fabrication

• Challenges in defect detection

• Importance of predictive maintenance

• Case Studies

This module focuses on AI integration in semiconductor fabrication and production, covering Application-Specific Integrated Circuits (ASICs), wafer processing and yield enhancement. Students will explore real-time monitoring, control systems and defect detection methods, with key terms including photolithography, plasma etching and chemical vapor deposition (CVD). They will also learn AI-driven techniques for yield enhancement and process control, including defect detection, root cause analysis and process optimization, with an emphasis on Statistical Process Control (SPC), process capability analysis and Six Sigma methodologies.

Weeks 29-32 :-

• Importance of yield enhancement in semiconductor manufacturing

• Role of defect detection in yield enhancement

• Role of root cause analysis in manufacturing

• Importance of real-time monitoring for yield enhancement

• Case Studies

This module delves into advanced machine learning techniques such as deep learning, reinforcement learning, and transfer learning, with applications in complex semiconductor manufacturing challenges. Students will explore how convolutional neural networks (CNNs), recurrent neural networks (RNNs) and reinforcement learning agents can be applied to enhance manufacturing processes. The module emphasizes cutting-edge AI methods to address intricate problems in semiconductor fabrication and design, enabling students to develop innovative solutions for industry-specific issues.

Weeks 33-36 :-

• Overview of advanced ML techniques
• Basics of reinforcement learning
• Concept of transfer learning
• Importance of process simulation & modelling
• Case Studies

This module explores innovative AI-driven processes and their integration into existing semiconductor manufacturing workflows. Topics include Industry 4.0, smart manufacturing, and digital twins, emphasizing how cyber-physical systems and IoT integration can transform production. Students will learn about data analytics for process innovation, predictive maintenance, and the use of AI to streamline and enhance manufacturing efficiency. Key terminologies include machine-to-machine (M2M) communication, real-time data analytics, and predictive modelling.

Weeks 37-40 :-

• Role of AI in driving process innovation

• Importance of integrating AI in semiconductor processes

• Role of AI in achieving sustainability

• Current state & future of AI in process innovation

• Case Studies

In the capstone project, students will apply their knowledge to real-world semiconductor manufacturing problems, leveraging AI to propose innovative solutions. They will work on comprehensive projects that require problem-solving, critical thinking, and collaboration. This module involves project planning, execution, and presentation of results, ensuring students can integrate and demonstrate their skills in AI and semiconductor manufacturing. Key aspects include defining project objectives, data analysis, model development, and implementing AI-driven improvements in semiconductor processes.

Weeks 41-44 :-

• Project Planning & Proposal
• Research & Development
• Implementation & Testing
• Presentation & Evaluation