NUS accelerates AI for semiconductors through industry collaboration and new talent training specialisation

NUS accelerates AI for semiconductors through industry collaboration and new talent training specialisation

The University deepens research collaboration with Applied Materials to accelerate semiconductor process development, and introduces a new postgraduate specialisation that builds AI-ready talent for the industry

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The National University of Singapore (NUS) is advancing on two fronts to apply artificial intelligence (AI) across the semiconductor sector. The University, through the Applied Materials-NUS Advanced Materials Corporate Lab, will focus on research efforts that harness AI to accelerate semiconductor process development. In parallel, beginning in August 2026, NUS will introduce a new Applied AI for Materials and Process Engineering specialisation in its Master of Science in Semiconductor Technology and Operations (MSc STO) programme, offered by the College of Design and Engineering at NUS (NUS CDE). Together, these initiatives position NUS to play a leading role in advancing the integration of AI and chip manufacturing through both research and education.

The new research collaboration between NUS and Applied Materials aims to shorten one of the most expensive bottlenecks in chipmaking: the long cycle of trial and error needed to develop and optimise new materials and processes. By training AI on data generated from the Corporate Lab's processing equipment, the partners aim to build a system that can predict the most promising experiments to perform, speeding the path from the laboratory to the production line and reducing costly trial-and-error cycles.

The initiative also aligns with national and global priorities, including the Semiconductor Research, Innovation and Enterprise (RIE) Flagship launched under RIE2030. It comes as the global chip industry heads towards US$1 trillion in annual revenue, with an additional US$300 billion of potential upside from generative AI. Singapore plays an outsized role, where it produces one in 10 chips worldwide, with the sector accounting for nearly six per cent of the country's GDP and has drawn over S$30 billion in semiconductor investment between 2022 and 2025.

The Corporate Lab, launched in 2018 and expanded in 2024, spans applied chemistry, materials science and semiconductor process engineering. Harnessing NUS' strengths in materials science, engineering and AI, alongside Applied Materials' expertise in semiconductor equipment and advanced manufacturing processes as well as Singapore's mature semiconductor ecosystem, there is now an opportunity to close a critical industry gap. AI has transformed materials discovery in many fields, but has had limited impact on semiconductor manufacturing, due to the complex set of processing parameters and possible materials outcomes at different scales, which is difficult for general material-discovery models to capture.

"Semiconductors are fundamental to today's AI, and now AI is transforming how semiconductors themselves are designed and made. That makes ever-closer collaboration between universities and industry essential, both to turn research into real-world impact and to prepare graduates for the roles this shift is creating," said Professor Aaron Thean, NUS Deputy President (Academic Affairs) and Provost. "Deepening our collaboration with Applied Materials, together with our new AI specialisation in semiconductor engineering education, reflects how NUS is advancing this sector on both fronts, through research that forges new frontiers and education that nurtures the talent to apply it."

"Accelerating semiconductor innovation requires materials engineering, process technology and AI to come together as one system," said Dr Prabu Raja, President of the Semiconductor Products Group at Applied Materials. "By combining NUS' strengths in AI and materials science with Applied Materials' process equipment expertise and real-world data, we can significantly reduce development cycles and speed innovation from lab to fab. Just as important, this collaboration helps prepare a new generation of engineers to operate at the intersection of AI and semiconductor manufacturing."

To mark the start of the collaboration, a Memorandum of Understanding was signed today by Professor Aaron Thean, NUS Deputy President (Academic Affairs) and Provost, and Mr Brian Tan, Regional President (South East Asia), Applied Materials, in conjunction with the opening of the Applied Materials Tampines Campus. The signing ceremony was witnessed by Mr Gary Dickerson, President and CEO of Applied Materials, and Mr Png Cheong Boon, Chairman of the Singapore Economic Development Board.

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Closing the gap between AI and chip manufacturing

The new research collaboration between NUS and Applied Materials would address three key challenges in implementing AI in semiconductor manufacturing: the complexity of processing parameters in materials development, the fragmented data generated during semiconductor manufacturing, and understanding how minute structural changes in materials impact device performance.

The aim is to develop an AI platform that learns from both simulations and real experiments and recommends the next best experiment to run, resulting in a closed loop that steadily narrows the search for better materials and process conditions.

"AI has already spurred materials discovery in many fields, but it has not yet reached the factory floor in semiconductors, where the messy physics of real equipment often stymies its implementation," added Prof Thean. "By coupling our physics-informed AI with Applied Materials' tools and the Corporate Lab's advanced processing capabilities, we can build models that understand how a process actually behaves, and use them to point researchers to the experiments most likely to pay off."

Building AI-ready semiconductor talent

The new Applied AI for Materials and Process Engineering specialisation extends the MSc STO programme into a rapidly growing field where AI is transforming materials innovation, semiconductor manufacturing, and advanced engineering operations.

Designed for STEM graduates and early- to mid-career professionals, the specialisation equips students to apply data-driven and computational methods to real industrial problems. Students will gain hands-on experience with technologies such as machine learning, generative AI, computer vision, semiconductor technologies, and digital twins through practical applications such as defect detection, predictive maintenance, yield optimisation, and materials characterisation.

Students will also have the opportunity to work on cutting-edge projects through placements in the industry. These placements span sectors such as semiconductors, materials and process engineering, and advanced manufacturing and operations, enabling students to develop interdisciplinary expertise, apply AI to real-world challenges, and build industry-relevant skills for careers in emerging technology sectors.

The curriculum emphasises human-centricity, ensuring that students retain responsibility for decision-making and applying domain knowledge even as they leverage AI tools. By combining human expertise and critical thinking with AI-driven insights, graduates will be equipped to accelerate innovation, enhance operational efficiency, and drive competitive advantage across the semiconductor industry.

Prof Thean said, "This new AI specialisation represents a timely convergence of education and innovation. We are building a pipeline of AI-ready talent for an industry vital to our technological future. As we integrate AI across education and research, we maintain a core principle: humans remain in the driver's seat. AI is a tool to augment, not replace, human ingenuity and judgment. This reflects NUS' commitment to empowering our community to harness AI responsibly while preserving the critical thinking and creativity at the heart of innovation."

The MSc STO programme is part of a comprehensive range of engineering courses offered by NUS CDE at both undergraduate and graduate levels designed to build the semiconductor talent pipeline. Covering disciplines such as electrical engineering, materials science and engineering, mechanical engineering, as well as industrial systems engineering and management, these courses provide diverse opportunities for students to make impactful contributions to the sector.

NUS' new research collaboration with Applied Materials and AI training specialisation complement existing efforts strengthen Singapore's position at the leading edge of the global semiconductor industry as it converges with AI. Together, they advance both the research that drives the field forward and the talent that sustains it.