Applied Materials is expanding its university partnerships in Singapore as chipmakers turn to artificial intelligence (AI) to cut development time and run factories with fewer surprises.
It announced separate collaborations with the National University of Singapore (NUS) and the Singapore Institute of Technology (SIT) on Wednesday, alongside the opening of its new campus in Tampines.
The work links research and training to two problems in chipmaking: the long cycle of experiments needed to develop new materials and processes, and the need for engineers who can work across manufacturing, AI and automation.
The announcements come as Singapore seeks to maintain its role in the global chip supply chain. NUS says Singapore produces one in 10 chips worldwide, with the semiconductor sector accounting for nearly 6% of gross domestic product. The country attracted more than $30 billion in semiconductor investment between 2022 and 2025, according to NUS.
AI in process development
Applied Materials and NUS will deepen research through the Applied Materials-NUS Advanced Materials Corporate Lab, which was launched in 2018 and expanded in 2024. The lab spans applied chemistry, materials science and semiconductor process engineering.
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The partners plan to train AI on data generated from the corporate lab’s processing equipment. The aim is to build a system that can predict the most promising experiments to run, reducing trial-and-error cycles and speeding the path from laboratory work to production.
NUS says the research will address three challenges in applying AI to semiconductor manufacturing. This includes the complexity of processing parameters in materials development, fragmented data generated during manufacturing and the need to understand how small structural changes in materials affect device performance.
Prabu Raja, president of Applied Materials’ Semiconductor Products Group, describes the precision required in chipmaking at the media briefing. “One angstrom matters,” he says, referring to one-tenth of a nanometre. “One angstrom can make a big improvement in performance or power.”
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The aim is to develop an AI platform that learns from simulations and real experiments, then recommends the next experiment to run.
“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.
Raja adds: “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.”
Industry-backed professorship
In a separate announcement, SIT said Applied Materials is backing a $3 million endowed gift for its first industry-based professorship and a new scholarship for engineering undergraduates.
Valued at $1 million, the Applied Materials Professorship will focus on semiconductor technologies, AI and robotics. Stefan Winkler, a professor in SIT’s Engineering Cluster, has been appointed the inaugural Applied Materials Professor from academic year 2026.
Winkler will lead research across semiconductor manufacturing, integrated service and maintenance operations. His work will focus on robotics and agentic AI, including rare-defect detection, smarter maintenance, equipment performance, and precision automation.
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SIT says the research aims to support more autonomous, AI-driven and self-optimising manufacturing systems. Intended outcomes include reducing unplanned downtime and recovery cycles, enabling proactive intervention before failures occur, and improving throughput and yield.
“Semiconductor manufacturing today faces real challenges, such as detecting rare defects, managing process variability and enabling precision automation, and these are the areas where applied research can make a meaningful difference,” says Winkler.
Talent programmes
NUS will introduce an Applied AI for Materials and Process Engineering specialisation from August 2026 under its Master of Science in Semiconductor Technology and Operations programme.
The specialisation is designed for STEM graduates and early- to mid-career professionals. Students will work with technologies such as machine learning, generative AI, computer vision, semiconductor technologies and digital twins.
The course will cover applications including defect detection, predictive maintenance, yield optimisation and materials characterisation. Students will also have the opportunity to work on industry projects through placements. The curriculum will emphasise human responsibility in decision-making while using AI tools, too.
At SIT, a $2 million endowed scholarship will support engineering undergraduates from financially disadvantaged backgrounds.
The scholarship is worth $15,000 per recipient and will cover educational expenses, including tuition fees, course materials, overseas immersion programmes and other university-organised educational activities.
Four scholarships will be awarded in academic year 2026, followed by five in 2027 and at least six from 2028.
Eligible students must be Singapore citizens or permanent residents, have outstanding academic results and come from households with a monthly per capita income of $2,500 or less.
