| Week |
Course Content |
| Week 1 |
Introduction to Quantum Devices —Introduction to quantum concepts quantum dots, nanowires, tunneling diodes. Discuss quantum confinement and its relevance in nanoelectronics. |
| Week 2 |
Quantum Devices (continued)—Explore the impact of quantum effects on modern semiconductor design and functionality. |
| Week 3 |
Semiconductor Basics—Cover energy band structure, charge carriers, and doping. Discuss semiconductor device operations |
| Week 4 |
Introduction to Machine Learning (ML)- Overview of ML—data, features, training, testing, and prediction; introduce basic algorithms. |
| Week 5 |
Introduction to Machine Learning (continued)- Real-world ML examples and case studies in materials and device prediction. |
| Week 6 |
Getting Started with Python for ML-Python basics: syntax, data handling (NumPy, Pandas), and visualization (Matplotlib). Work in Google Colab/Jupyter Notebook. |
| Week 7 |
Python for ML (continued)- Implement basic ML tasks using Scikit-learn. Practice regression and classification models with example datasets. |
| Week 8 |
ML for Materials and Property Prediction-Learn how ML predicts bandgaps, conductivity, and carrier mobility. Understand dataset preparation and feature engineering. |
| Week 9 |
ML for Materials (continued)- Build ML models to predict electronic and optical properties. Evaluate model accuracy. |
| Week 10 |
ML Applications in Materials Science-Explore databases (Materials Project, AFLOW). Use data for materials classification and discovery. |
| Week 11 |
Quantum Dots and ML Models-Study size-dependent quantum dot properties. Train ML models to predict quantum confinement effects. |
| Week 12 |
Applications of ML in Semiconductor Research-Discuss industrial and academic case studies. Focus on ML-assisted fabrication, defect analysis, and device optimization. |
| Week 13 |
Mini Project Introduction-Form teams, define project topics, and outline datasets and methods. Mentor-guided proposal discussion. |
| Week 14 |
Mini Project Development-Independent/group work: data collection, model training, and testing. Weekly feedback sessions. |
| Week 15 |
Mini Project Completion-Prepare project reports and presentations. Emphasis on result validation and visualization. |
| Week 16 |
Project Presentations and Evaluation-Final presentations, peer evaluations, and discussion of ML’s future in semiconductor research. |
self-directed
learning |
03.Preparing presentations or reports related to industry and academia.
|