The AI Materials Engineer: A New Playbook for Semiconductor R&D

Course Overview

Artificial Intelligence is transforming how semiconductor materials are researched, developed, characterized, and optimized. As semiconductor devices continue to shrink and new architectures emerge, traditional materials development approaches are increasingly challenged by the complexity of atomic-scale interactions, interfaces, defects, and process chemistry.

This course introduces semiconductor engineers, materials scientists, researchers, and technical managers to the rapidly evolving role of AI in materials engineering and semiconductor R&D.

Participants will learn the fundamentals of artificial intelligence, machine learning, neural networks, and generative AI, and explore how these technologies can accelerate materials discovery, predict material properties, identify defects, and optimize semiconductor manufacturing processes.

Through semiconductor-specific applications and case studies, participants will examine how AI can complement computational modeling and laboratory experimentation to improve research productivity, reduce development time, and support better engineering decisions.

The course emphasizes practical understanding, real-world semiconductor applications, and the importance of evaluating AI model accuracy and reliability.

Course Curriculum

1. Introduction to Artificial Intelligence

• What is Artificial Intelligence (AI)?
• Understanding machine learning (ML) and deep learning.
• Neural networks and how they function.
• Generative AI and large language models.
• How AI models learn from training data.
• Supervised, unsupervised, and reinforcement learning.
• Training data, algorithms, and model development.
• Overfitting, uncertainty, and prediction limitations.
• Understanding AI accuracy, validation, and reliability.
• Opportunities and limitations of AI in semiconductor engineering.

2. AI for Materials Modeling and Discovery

• Introduction to computational materials science.
• Understanding atomic-scale materials modeling.
• From quantum mechanical calculations to machine learning models.
• Density Functional Theory (DFT) and its role in materials research.
• Machine learning interatomic potentials.
• Molecular dynamics simulations.
• Predicting material properties using AI.
• Materials screening and accelerated materials discovery.
• Understanding material structure and property relationships.
• Data quality, model training, and validation.
• Active learning and intelligent experimental design.
• Combining AI predictions with computational and laboratory experiments.

3. AI Applications in Semiconductor Manufacturing and R&D

• AI-assisted discovery of new semiconductor materials.
• Predicting electrical, thermal, optical, and mechanical properties.
• Materials selection for advanced semiconductor devices.
• AI applications in advanced packaging and heterogeneous integration.
• Predicting and understanding defects in semiconductor materials.
• Modeling material interfaces and interfacial reactions.
• AI applications in thin-film deposition processes.
• Surface chemistry in ALD, CVD, and PVD.
• AI applications in plasma etching and surface reactions.
• Predicting material behavior during semiconductor processing.
• AI-assisted optimization of process conditions.
• Planning data collection and experimental validation.
• Semiconductor industry case studies and research examples.

4. Evaluating AI Models and Implementing AI in R&D

• How to determine whether an AI prediction is trustworthy.
• Understanding model uncertainty and prediction limits.
• Data collection and preparation for materials research.
• Experimental verification of AI-generated predictions.
• Combining AI with traditional materials engineering.
• Challenges of implementing AI in semiconductor R&D organizations.
• Opportunities for improving research productivity and development cycles.
• Future directions for AI-driven materials engineering.

5. Course Review and Discussion

• Review of AI and machine learning fundamentals.
• Identifying opportunities for AI in semiconductor materials development.
• Evaluating potential AI applications within participants’ organizations.
• Questions, discussion, and industry perspectives.

Who Should Attend?

This course is designed for professionals involved in semiconductor research, materials development, device engineering, process development, and advanced manufacturing.

Recommended participants include:

• Semiconductor materials engineers.
• Materials scientists and researchers.
• Semiconductor process engineers.
• Research and development engineers.
• Device and integration engineers.
• ALD, CVD, PVD, and plasma etch engineers.
• Advanced packaging and heterogeneous integration engineers.
• Computational materials scientists.
• Semiconductor equipment and process development engineers.
• Engineering managers and R&D directors.
• Technical leaders responsible for innovation and new materials development.
• University researchers and graduate-level technical professionals.

No previous AI, machine learning, or computer programming experience is necessary.

Who Will Benefit Most from This Course?

1. Semiconductor Materials Engineers

Learn how AI can accelerate the identification, screening, and development of new materials with improved electrical, thermal, mechanical, and chemical properties.

2. Semiconductor Process Engineers

Understand how AI can help predict material behavior, surface reactions, deposition chemistry, etch performance, and process optimization.

3. Research and Development Scientists

Discover how AI can reduce the time required to investigate new materials, evaluate research hypotheses, and identify promising experimental directions.

4. Device and Integration Engineers

Explore how AI can assist in understanding material interfaces, defects, scaling challenges, and material compatibility in advanced semiconductor architectures.

5. Advanced Packaging Engineers

Understand opportunities for AI-assisted materials selection and optimization involving chiplets, interposers, bonding materials, thermal management, and heterogeneous integration.

6. Engineering Managers and R&D Directors

Learn how AI can improve R&D productivity, reduce development costs, accelerate innovation, and help determine where AI investments can deliver meaningful value.

7. Semiconductor Equipment Engineers

Gain insight into how AI-based materials modeling can improve understanding of deposition, etching, plasma interactions, chamber materials, and equipment-related process challenges.

Course Benefits – What Participants Will Learn

Upon completion of this course, participants will be able to:

1. Understand AI Fundamentals: Explain the basic principles of AI, machine learning, neural networks, and generative AI.
2. Recognize AI Opportunities: Identify where AI can improve semiconductor materials research, development, and manufacturing.
3. Understand Materials Modeling: Explain how AI connects quantum calculations, machine learning interatomic potentials, and molecular simulations.
4. Accelerate Materials Discovery: Understand how AI can help identify promising materials and predict their properties before extensive laboratory testing.
5. Improve Process Development: Recognize AI applications in ALD, CVD, PVD, plasma etching, and semiconductor surface chemistry.
6. Understand Defects and Interfaces: Explore how AI can assist in identifying material defects, understanding interfaces, and predicting material performance.
7. Evaluate AI Reliability: Understand model validation, data quality, uncertainty, overfitting, and the limitations of AI-generated predictions.
8. Improve R&D Productivity: Identify opportunities to reduce experimental cycles, improve research efficiency, and accelerate materials development.
9. Make Better Engineering Decisions: Learn how to combine AI predictions with traditional engineering knowledge, computational modeling, and experimental verification.
10. Prepare for the Future: Develop a foundation for participating in AI-driven semiconductor materials research and development.