AI Material Matters
AI fuelling rapid race for new chip-making materials, AI-powered autonomous experimentation, or AI/AE, driving paradigm shift in materials science for semiconductors and beyond
AI fuelling rapid race for new chip-making materials
AI-powered autonomous experimentation, or AI/AE, driving paradigm shift in materials science for semiconductors and beyond
The US Department of Commerce has announced an open competition to demonstrate “how AI can assist in developing new sustainable semiconductor materials and processes that meet industry needs and can be designed and adopted within five years.”
Under Secretary of Commerce for Standards and Technology Laurie Locascio calls this “a unique opportunity to make the United States a world leader in efficient, safe, high-volume, and competitive semiconductor manufacturing.” Locascio is also director of the National Institute of Standards and Technology.
Up to US$100 million will be awarded by the CHIPS Research and Development Office (CHIPS R&D) to winners who “develop university-led, industry-informed, collaborations about artificial intelligence-powered autonomous experimentation (AI/AE) relevant to sustainable semiconductor manufacturing.”
CHIPS R&D was established by the US CHIPS and Science Act, which US President Joe Biden signed into law in August 2022. The law provides the Department of Commerce with $50 billion for programs intended to strengthen and revitalize US semiconductor manufacturing and R&D.
Of that amount, $39 billion went to the CHIPS Program Office for investment in facilities and equipment in the United States, including high-profile factories being built by Taiwan’s TSMC and America’s Intel. $11 billion was allocated to CHIPS R&D for projects such as this one.
“For the US semiconductor industry to flourish in the long-term,” the Commerce Department writes, “it must be able to develop innovative and commercially competitive technologies to sustainably produce materials and manufacture chips in a way that protects the environment and local communities.”
That seems obvious. After all, the leading American producer of semiconductor production equipment named itself Applied Materials while semiconductor makers invest billions every year to design more advanced integrated circuits, use electric power and water more efficiently in the production process and reduce industrial waste and greenhouse gas emissions.
But Commerce Secretary Gina Raimondo has a newfound sense of urgency. “Right now,” she says, “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.”
Raimondo also has a sense of mission, stating, “With this new program, the Biden-Harris administration will harness the vast capabilities of AI to unleash the full potential of our workers and innovators while building a more secure and enduring domestic semiconductor industry.”
Apart from making extravagant claims for an amount of money that seems like a drop in the bucket compared with the billions of dollars the semiconductor industry spends every year on R&D, what are these announcements all about? (In the second quarter of this year alone, Intel’s R&D budget was $4.2 billion.)
The answer is that AI/AE, which combines machine learning and automated laboratories, is “ushering in a paradigm shift in materials science,” according to Taro Hitosugi, Ryota Shimizu and Naoya Ishizuki of the Tokyo Institute of Technology. “Using computer algorithms and robots to decide and perform all experimental steps, these systems require no human intervention.”
“Given the possible combinations of elements,” they continue, “there is an almost infinite number of new materials… Thus, optimising high-dimensional synthesis parameters in a vast search space is necessary for materials synthesis… In a way, the world of materials is a frontier for exploration, much like space or the deep sea.”
AI/AE should enable a vast acceleration of the process of materials discovery and synthesis, not only in the semiconductor industry but across the spectrum of applied science, from electronics, energy, aerospace and defense to biology, chemistry and pharmaceuticals.
Writing in Nature Synthesis, Milad Abolhasani of North Carolina State University and Eugenia Kumacheva of the University of Toronto state that:
The recent growth of data science and automated experimentation techniques has resulted in the advent of self-driving labs (SDLs) via the integration of machine learning, lab automation and robotics.
An SDL is a machine-learning-assisted modular experimental platform that iteratively operates a series of experiments selected by the machine-learning algorithm to achieve a user-defined objective. These intelligent robotic assistants help researchers to accelerate the pace of fundamental and applied research through rapid exploration of the chemical space.
The main impact of SDLs is the ‘research acceleration’ to generate new knowledge that leads to the discovery of novel compounds or manufacturing routes of the best-performing materials 10–1,000 times faster than by utilizing one-at-a-time variable exploration or combinatorial experiments.
In other words, AI and robots can do the job much more efficiently than scientifically informed trial and error. Researchers led by Professor Alán Aspuru-Guzik of the Department of Chemistry at the University of Toronto write:
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, namely from an estimated ten million dollars and ten years of development to one million dollars and one year… the solution for this challenge is the development of self-driving laboratories… The Aspuru-Guzik group sees in these laboratories the potential to increase the rate of experimentation and scientific discovery, which will eventually change the way we do science.
Aspuru-Guzik is also a professor of computer science, holds a Google Industrial Research Chair in Quantum Computing and is director of the Acceleration Consortium, a University of Toronto-based strategic initiative that aims to accelerate “the discovery of materials and molecules needed for a sustainable future” by bringing together researchers from industry, government and academia.
This may be the model for the US Commerce Department’s semiconductor materials initiative. In addition, the department’s AI/AE competition bears a strong resemblance to the SDL Grand Challenge proposed by the Washington-based Center for Strategic and International Studies (CSIS) think tank in its January 2024 report entitled “Self-Driving Labs: AI and Robotics Accelerating Materials Innovation.”
Declaring that “The development and adoption of alternative and new materials is central to US leadership in emerging technologies,” the CSIS report wonders “whether the United States is devoting sufficient policy attention and resources to securing the advantage in SDLs.”
At that time, according to CSIS, US spending on SDLs was less than $50 million and “not done in a directed, programmatic manner,” while Canada had awarded $200 million to the Acceleration Consortium at the University of Toronto.
In this context, the US Commerce Department’s $100 million award will be a belated but meaningful step forward. Its five-year time frame matches the semiconductor industry’s roadmap to 1nm process technology.
CSIS also reported that the University of Liverpool, Lawrence Berkeley National Lab, Argonne National Lab and Carnegie Mellon University were building SDLs, noting that “University of Liverpool researchers in 2020 used a mobile platform robot arm to synthesize and search for catalysts across 10 design parameters, ultimately conducting 688 experiments over eight days completely autonomously and identifying chemical formulations that were 6 times better than the baseline.”
Imec, the Inter-university Microelectronics Centre headquartered in Belgium that conducts advanced R&D with and for the semiconductor industry, is using AI to identify new materials. For example, scientists affiliated with imec write:
With decreasing dimensions and increasing complexity, semiconductor devices are getting more difficult to fabricate. In particular, the allowed deposition temperature becomes lower. Amorphous materials, which do not require annealing steps, are therefore becoming more interesting.
First principles modeling of amorphous materials is, however, way more complex than modeling crystalline ones. Especially to screen for new materials, a fully ab initio approach is hence too expensive. We take on this challenge by employing a combination of high throughput first principles calculations and artificial intelligence (AI).
The Johns Hopkins University Applied Physics Laboratory (APL) is using AI to accelerate the discovery of new materials that can withstand the extreme environments that characterize deep-sea exploration, space exploration, hypersonic vehicles and other uses related to national security.
Morgan Trexler, program manager for Science of Extreme and Multifunctional Materials at APL says,
As the US faces pressing national security challenges, there are increasing operations in austere environments – and those operations require revolutionary new materials. We cannot wait decades to discover materials that meet those needs. By infusing AI approaches throughout the discovery process, we can more quickly and intentionally identify materials for complex, specific applications.
Keith Caruso, chief scientist at APL’s Research and Exploratory Development Department, adds that “The approach to building on existing materials will only ever yield limited improvements. To create groundbreaking materials, we need to make a fundamental leap.”
In Japan, the RIKEN National Research and Development Agency is applying high-performance computing and AI to drug discovery and genomic medicine.
Japanese analytical instrument maker Shimadzu Corporation, which works with Kobe University, is targeting “a platform for autonomous scientific discoveries by robots and AI” as its vision of future laboratories for the development of new materials, pharmaceuticals and biotechnology, including “smart cells” with altered genes.
Science China Press reports that “Following the success of large language models, the concept of large materials models as deep-learning computational models for materials design has attracted great interest.”
Researchers from Tsinghua University are seeking to develop models “capable of handling diverse material structures across most elements of the periodic table.”
Almost a year ago, China Daily reported that an AI-driven robotic chemist developed by Chinese scientists had synthesized a catalyst to generate oxygen from Martian meteorites.
“A stress test at minus 37 degrees Celsius, which mimics the temperature condition on Mars, showed that the catalyst can steadily produce oxygen without apparent deterioration, suggesting that it can work in the harsh conditions on Mars.”
What the Chinese are doing in AI-driven autonomous materials development for the semiconductor and other industries is not clear, but they are unlikely to ignore its possibilities.
Follow this writer on X: @ScottFo83517667
https://asiatimes.com/2024/10/ai-fueling-rapid-race-for-new-chip-making-materials/
Hidden Champions
Interview: Leading Economist says Chinese market crucial for German firms
by Xinhua writer Li Hanlin
BERLIN, Oct. 3 (Xinhua) -- "German investments in China reached a new record in the first half of 2024, continuing the upward trend from 2023, when investment levels also set new benchmarks," Hermann Simon, a prominent German economist known as the father of the "hidden champions" theory, told Xinhua in a recent interview.
This year marks the 10th anniversary of the comprehensive strategic partnership between China and Germany. Simon said that the economic and trade relations between the two countries have deepened significantly while cooperation remains the dominant force, and both sides have demonstrated substantial potential for mutual learning and win-win outcomes.
"Business people make their decisions based on assessments of markets and costs," Simon said, adding that the continued increase in German investments in China has shown that "China is such an important market for the German companies that they keep investing there."
As an economist, Simon first introduced the concept of "hidden champions" -- small and medium-sized enterprises (SMEs) that lead global niche markets but are not widely known -- these companies achieve market leadership through focused strategies, ongoing internationalization, and continuous innovation.
"China's hidden champions have become one of the main competitors of German hidden champions," Simon noted. From his perspective, both German and Chinese economies are engineering-driven, as opposed to the consumer market-oriented economy of the United States. This makes Chinese and German firms natural competitors as well as significant partners.
Simon emphasized the critical role that business ecosystems play in the development of hidden champions. These ecosystems, formed through long-term cooperation and shared expertise among multiple companies, enable SMEs to accomplish complex products and systems that would be difficult to achieve independently. As a result, there is ample room for mutually beneficial cooperation between Chinese and German companies within this framework.
The growth of China's hidden champions is seen as part of its high-quality economic development path. Simon praised Chinese companies for their innovation and leadership in fields such as telecommunications and railway transportation.
"For me, patents are indicators of future industrial competitiveness. We see that with international patents, China has become number one. They just became one of the top ten in 2004, and since then, China has moved forward every year and is now number one, ahead of the United States," Simon said.
"China is considered a competence centre," Simon noted, adding that in high-tech sectors, many German hidden champions have established research and development centres and artificial intelligence hubs in China, as they believe China has superior conditions for developing AI products and processes, viewing China as a future industrial incubator.
China has emerged as the most important market for German "hidden champion" companies, reinforcing Simon's confidence in the future of the Chinese economy.
Highlighting the immense potential of the Chinese market, he said that while Germany currently hosts approximately 1,500 hidden champion companies, the largest concentration in the world, the number of such companies in China is rapidly growing.
These global market leaders, driven by entrepreneurship and a relentless pursuit of excellence, are poised to continue propelling China's economic growth, he added.
