Programme Parameters
Graduate / Intermediate
4 Weeks (40 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI for Next-Generation Semiconductor Material Discovery shows how machine learning is accelerating the search for the materials behind future electronics. You learn how the vast space of candidate semiconductor materials — beyond silicon, into compound, 2D and wide-bandgap materials — is explored with materials informatics: predicting electronic properties, screening candidates, and guiding design toward target characteristics. The course connects data-driven discovery to the realities of semiconductor R&D, from data quality to experimental validation. You finish able to reason about applying AI to a materials-discovery problem in electronics. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course applies AI to semiconductor material discovery — using machine learning and materials informatics to predict, screen and design next-generation electronic materials.
📋 Course Objectives
1. Explain the semiconductor materials design space.
2. Predict electronic properties with machine learning.
3. Screen candidate materials at scale.
4. Guide design toward target characteristics.
5. Connect discovery to experimental validation.
👥 Who Should Enroll?
• Materials scientists and semiconductor researchers
• Data scientists in materials and electronics
• Semiconductor R&D professionals
• Students of materials informatics
🚀 Key Learning Outcomes
• An understanding of AI in semiconductor discovery.
• The ability to reason about materials informatics.
• A foundation in data-driven materials design.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
AI Fundamentals, Mathematics, and Foundations
Apply mathematical concepts such as linear algebra and calculus to solve problems in AI for semiconductor material discovery • Develop a strong foundation in programming languages such as Python and R for AI applications • Analyze the role of AI in next-generation semiconductor material discovery and its potential impact on the industry
Data Engineering, Preprocessing, and Feature Pipelines
Design and implement data pipelines to preprocess and feature-engineer large datasets for semiconductor material discovery • Configure data storage solutions such as relational databases and NoSQL databases for efficient data retrieval • Evaluate the quality and integrity of datasets used in AI models for semiconductor material discovery
Model Architecture, Algorithm Design, and Methods
Develop and implement deep learning models such as convolutional neural networks and recurrent neural networks for semiconductor material discovery • Optimize model architectures using techniques such as transfer learning and hyperparameter tuning • Analyze the performance of different AI algorithms and models for semiconductor material discovery
Training, Hyperparameter Optimization, and Evaluation
Train AI models using large datasets and evaluate their performance using metrics such as accuracy and precision • Implement hyperparameter optimization techniques such as grid search and random search to improve model performance • Configure and deploy AI models in cloud-based environments such as AWS and Google Cloud
Deployment, MLOps, and Production Workflows
Design and implement MLOps pipelines to deploy and manage AI models in production environments • Develop and deploy containerized AI applications using Docker and Kubernetes • Evaluate the performance and reliability of AI models in production environments
Ethics, Bias Mitigation, and Responsible AI Practices
Analyze the ethical implications of AI in semiconductor material discovery and develop strategies to mitigate bias • Develop and implement fairness metrics and algorithms to ensure responsible AI practices • Evaluate the transparency and explainability of AI models and develop techniques to improve them
Industry Integration, Business Applications, and Case Studies
Develop business cases and applications for AI in semiconductor material discovery • Analyze the economic and social impact of AI on the semiconductor industry • Evaluate the potential of AI to drive innovation and growth in the semiconductor industry
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Scikit-learn |
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