The US Department of Commerce recently announced an open competition designed to accelerate the discovery of sustainable semiconductor materials using artificial intelligence (AI). This initiative will allocate up to $100 million in funding to projects that leverage AI-driven autonomous experimentation (AI/AE) to create advanced, eco-friendly semiconductor manufacturing processes. The goal is to produce breakthroughs that can be scaled and adopted within five years, setting the stage for the United States to become a global leader in sustainable semiconductor production.
Laurie Locascio, Under Secretary of Commerce for Standards and Technology and Director of the National Institute of Standards and Technology (NIST), emphasized the significance of this initiative: “This is a unique opportunity to make the United States a world leader in efficient, safe, high-volume, and competitive semiconductor manufacturing.”
This competition, led by the CHIPS Research and Development Office (CHIPS R&D), seeks to foster collaborations between universities and industry experts. These collaborations will focus on developing AI-powered autonomous experimentation (AI/AE) systems that drive sustainable advancements in semiconductor materials and manufacturing processes. This initiative is part of a broader effort to revitalize the US semiconductor industry through sustainable innovation.
CHIPS R&D was established by the US CHIPS and Science Act, signed into law by President Joe Biden in August 2022. The legislation provides the Department of Commerce with $50 billion to bolster US semiconductor manufacturing and R&D. Of that total, $39 billion is dedicated to investing in domestic semiconductor manufacturing facilities and equipment, including significant projects such as factories built by Taiwan Semiconductor Manufacturing Company (TSMC) and Intel. The remaining $11 billion is allocated to CHIPS R&D, which will fund projects like the new AI competition.
Commerce Secretary Gina Raimondo stressed the urgency of accelerating sustainable innovation: “Right now, new semiconductor materials often take years to be production-ready and are incredibly resource-intensive. If we’re going to quickly build up America’s semiconductor manufacturing base in a way that’s sustainable over the long term in the face of increasing threats from the climate crisis, we need to leverage AI to help develop sustainable material processes quickly.”
The concept of using AI and machine learning in materials science is gaining traction across various industries. The semiconductor sector, which already invests billions annually in R&D to enhance chip performance, efficiency, and sustainability, stands to benefit significantly from AI-driven approaches. AI/AE, which integrates machine learning with automated experimentation, could revolutionize materials discovery by accelerating the process and reducing costs.
AI/AE systems allow machines to perform complex experimental steps autonomously, with minimal human intervention. This technology opens the door to faster exploration of new materials and processes, which can lead to breakthroughs across multiple fields, including semiconductors, electronics, energy, aerospace, and biotechnology.
According to experts Taro Hitosugi, Ryota Shimizu, and Naoya Ishizuki from the Tokyo Institute of Technology, “Given the possible combinations of elements, there is an almost infinite number of new materials… optimizing high-dimensional synthesis parameters in a vast search space is necessary for materials synthesis.” AI/AE systems excel in this area, enabling researchers to explore material possibilities much faster than traditional methods.
AI-powered autonomous labs are already being used in research environments. For instance, Milad Abolhasani of North Carolina State University and Eugenia Kumacheva of the University of Toronto describe these self-driving labs (SDLs) as machine-learning-assisted platforms that run a series of experiments designed by AI algorithms. These SDLs can accelerate materials discovery by a factor of 10 to 1,000 compared to conventional experimentation.
Professor Alán Aspuru-Guzik of the University of Toronto echoes this sentiment, emphasizing that AI can drastically reduce both the time and cost required to develop new materials. “In our research group, we aim to reduce the time and money required to discover a new functional material or optimize a known one by a factor of ten,” he states.
The potential for AI to revolutionize semiconductor manufacturing is immense. Self-Driving Labs (SDLs), which combine AI and robotics to automate experimentation, are already proving their value in materials discovery. In 2020, researchers from the University of Liverpool used an autonomous platform to conduct 688 experiments in just eight days, identifying chemical formulations six times more efficient than existing catalysts.
This kind of accelerated experimentation could play a critical role in the semiconductor industry’s shift toward sustainability. The Commerce Department’s initiative aligns with the industry’s roadmap for advancing process technology to smaller nodes, such as 1nm. With sustainability becoming a top priority, the development of new materials and manufacturing processes that reduce energy consumption, minimize waste, and limit the use of hazardous chemicals will be essential.
According to Imec, a leading research institute in microelectronics based in Belgium, the complexity of modern semiconductor devices demands new materials that can be synthesized with low environmental impact. Imec scientists are employing AI to model and identify such materials, emphasizing the potential of AI to address the challenges posed by shrinking process nodes and more intricate designs.
While the US is taking significant steps with this new competition, the use of AI in materials science is already gaining momentum globally. In Japan, the RIKEN National Research and Development Agency is applying AI to drug discovery and genomic medicine, while Shimadzu Corporation is working with Kobe University to develop autonomous scientific discovery platforms for materials and pharmaceuticals.
China is also making strides in this area. A Chinese-developed AI-driven robotic chemist recently synthesized a catalyst that can generate oxygen from Martian meteorites, potentially contributing to future space exploration. The development of AI-driven autonomous materials systems in China’s semiconductor industry remains less transparent, but the potential for innovation in this area is significant.
The $100 million funding announced by the US Commerce Department will undoubtedly have a positive impact on the US semiconductor sector. However, as noted by the Center for Strategic and International Studies (CSIS) in its January 2024 report on SDLs, this amount is still modest compared to the hundreds of millions being invested by other countries. For example, Canada has already awarded $200 million to the Acceleration Consortium at the University of Toronto, which focuses on advancing AI-driven experimentation for sustainable materials.
Nevertheless, the competition is a crucial step in ensuring the US keeps pace with global advancements in AI-powered materials discovery. The five-year timeline matches the industry’s push to develop the next generation of semiconductor technology, which will be critical for maintaining US leadership in advanced manufacturing.
The integration of AI and automation into semiconductor manufacturing offers exciting possibilities for reducing the environmental impact of one of the world’s most critical industries. By accelerating the discovery of new materials and optimizing manufacturing processes, AI/AE systems could help address some of the semiconductor industry’s most pressing sustainability challenges.
However, the success of this initiative will depend on strong collaboration between universities, industry leaders, and government agencies. As semiconductor technology becomes more complex and the need for sustainability more urgent, the role of AI in transforming materials science will only grow. The US Commerce Department’s competition is a step toward harnessing this potential and securing the future of semiconductor manufacturing in a rapidly evolving technological landscape.
In a world where semiconductors power everything from smartphones to satellites, the race to develop faster, more efficient, and sustainable manufacturing processes has never been more critical. By leveraging the capabilities of AI, the United States is positioning itself at the forefront of this transformation, setting the stage for a new era of semiconductor innovation.
