Workshop 1
Innovating Semiconductor Manufacturing: Science Meets AI
Organizer : Jaehoon Jeong (Samsung Electronics) Purpose of the Workshop
Jaehoon Jeong is a seasoned professional with over 17 years of experience in digital twin technology. He currently serves as a Group Leader at Samsung Electronics, where he has been driving innovation and advancing technological solutions since joining the company in 2007. Dr. Jeong earned his Ph.D. in Electronic Engineering from Texas A&M University in 2006. His career at Samsung began with PKG and PCB development, which later expanded to include transistor modeling and 3-D IC modeling. These experiences laid the foundation for his expertise in bridging cutting-edge technology with real-world applications. Dr. Jeong’s recent focus is on introducing science-based digital twins, a transformative approach that combines scientific principles with artificial intelligence to enhance predictive modeling and operational efficiency. His innovative work in this area aims to redefine the integration of science and AI in industrial and technological contexts. Throughout his career, Dr. Jeong has been recognized for his contributions to the field, leading projects that bridge the gap between cutting-edge research and practical application. His work continues to shape the future of digital transformation in the tech industry.
Language models have been used for generating new ideas and hypotheses in scientific domains. For instance, language models could suggest new drugs or engineering designs. However, this is not sufficient to attack the hard part of science which is the physical experiments needed to validate the proposed ideas. This is because language models lack physical validity and the ability to internally simulate the processes. Traditional simulation methods are too slow and infeasible for complex processes observed in many scientific domains. We propose AI-based simulation methods that are 4-5 orders of magnitude faster and cheaper than traditional simulations. They are based on Neural Operators which learn mappings between function spaces.
Machine learning (ML) and deep learning (DL) have been pivotal in solving complex problems across physics, chemistry, and engineering. In semiconductor process and device design, the integration of domain-specific knowledge into ML/DL models is essential due to the limited availability of data, bridging the gap between theoretical simulations and practical applications.
Artificial Intelligence is an avenue to innovation that is touching every industry worldwide. AI has made rapid advances in areas like speech and image recognition, gaming, and even self-driving cars. In the area of chip design, we see a growing gap in what designer’s want to achieve and what the available resources and time allow. This presentation will provide an overview of recent AI trends, show how the latest AI technology including Generative AI can be applied to optimize and automate chip design tasks from architectural design to digital and analog implementation, optimizing silicon life cycles and improving yield. We will also discuss future AI trends and opportunities to accelerate time to market, improve productivity and achieve better quality of results.
Non-destructive metrology plays a critical role in the semiconductor manufacturing process, enabling precise monitoring and control of complex device architectures without compromising the integrity of wafers. As device dimensions continue to shrink and 3D architectures become increasingly complex, traditional metrology methods face significant challenges in terms of resolution, accuracy, and throughput. This presentation provides a comprehensive review of recent advancements in non-destructive metrology, with a particular focus on the integration of simulation and artificial intelligence (AI). By leveraging physics-based simulations to model complex processes and utilizing AI to analyze vast datasets and predict critical parameters, innovative methodologies have emerged that enhance measurement accuracy and process efficiency. Case studies highlighting the application of simulation-driven AI in areas such as SEM3D will be discussed, showcasing how these approaches address key challenges in advanced semiconductor manufacturing. This review aims to provide insights into the current state of the field, identify remaining limitations, and propose directions for future research and development.
The primary goal of this workshop is to explore how science-driven artificial intelligence (AI) can address the unique challenges of semiconductor manufacturing, an industry that demands both precision and innovation. By integrating fundamental scientific principles with advanced AI technologies, the workshop aims to:
- Facilitating Knowledge Exchange: Provide a platform to share breakthroughs in science-based AI applications for semiconductor processes.
- Promoting Cross-Disciplinary Collaboration: Bridge the gap between semiconductor researchers, AI scientists, and industry practitioners to address critical challenges.
- Fostering Science-Driven Innovation: Highlight how AI grounded in scientific methodologies can optimize manufacturing processes, accelerate material discovery, and enhance manufacturing efficiency.
Why This Workshop is Essential?
- Rising Complexity in Semiconductor Manufacturing: As chip designs become smaller, more powerful, and more complex, traditional manufacturing processes face limitations. Science-driven AI, informed by physics, chemistry, and material science, offers targeted solutions to these challenges by enabling more accurate modeling, prediction, and optimization.
- Industry Demand for AI Integration: The semiconductor industry is increasingly adopting AI to stay competitive. However, there is a need for a structured platform to discuss best practices, share case studies, and address barriers to adoption.
- Limitations of General AI Models: General-purpose AI models often lack the depth of understanding required for specialized applications like semiconductor manufacturing. When applied to complex problems with limited data, these models can produce results that violate physical laws or fail to account for critical scientific constraints. On the other hand, science-based AI mitigates these risks by embedding physical laws, chemical properties, and domain-specific knowledge into AI algorithms, ensuring accuracy and reliability.
- Accelerating Innovation: The convergence of AI and semiconductor manufacturing has the potential to revolutionize the industry, from advanced chip design to smart factories. Hosting this workshop can catalyze innovation by connecting stakeholders and fostering new ideas.
- Educational Impact: This workshop will provide valuable learning opportunities for participants, helping them stay at the forefront of technological advancements and preparing them for future challenges.
Proposed Topics
- An exploration of the necessity for science-driven AI integration.
- Strategies for efficiently instructing AI in scientific disciplines
- Illustrative applications of science-driven AI in the semiconductor manufacturing
The first topic was selected owing to Professor Jeong's profound expertise and prolific publication record, which provide rich insights into this area. The second topic is inspired by the unparalleled authority of Professor Anima, who stands as the foremost expert in the AI domain on this subject. For the third topic, we have enlisted a distinguished executive from a leading semiconductor company to elucidate the practical applications and transformative impact of science-based AI in the industry.
By implementing this structured operational plan, the workshop aims to provide a meaningful, impactful, and well-organized experience for all participants, fostering innovation and collaboration in the field of semiconductor manufacturing and AI.
About Organizer
ML and DL have driven significant advancements in semiconductor manufacturing. Applications include real-time prediction of doping profiles and current-voltage characteristics in MOSFETs, full-chip stress analysis using hybrid frameworks, and compact modeling for scalable device designs. These innovations not only enhance alignment with experimental results but also optimize device performance under constrained conditions. Strategies such as transfer learning, which employs pre-trained models to fine-tune tasks with limited data, further address challenges posed by data scarcity, expanding the scope of ML/DL applications in semiconductor engineering.