The National University of Singapore (NUS) is advancing on two fronts to apply artificial intelligence (AI) across the semiconductor sector, deepening research collaboration with Applied Materials to accelerate semiconductor process development and introducing a new postgraduate specialization that builds AI-ready talent for the industry.
NUS said in a statement on Wednesday that the University, through the Applied Materials-NUS Advanced Materials Corporate Lab, will focus on research efforts that harness artificial intelligence (AI) to accelerate semiconductor process development.
In parallel, beginning in August 2026, NUS will introduce a new Applied AI for Materials and Process Engineering specialization in its Master of Science in Semiconductor Technology and Operations (MSc STO) program, 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.
According to the statement, 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 optimize 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 $1 trillion in annual revenue, with an additional $300 billion of potential upside from generative AI.
It is noted that 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 gross domestic product (GDP) and has drawn over S$30 billion ($23.4 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.
NUS noted that 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 specialization 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,” he added.
Dr Prabu Raja, President of the Semiconductor Products Group at Applied Materials, said accelerating semiconductor innovation requires materials engineering, process technology and AI to come together as one system.
“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,” he added.
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