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    Let machines do the work: Automating semiconductor research with machine learning

    The development of new thin semiconductor materials requires a quantitative analysis of a large amount of reflection high-energy electron diffraction (RHEED) data, which is time consuming and requires expertise. To tackle this issue, scientists from Tokyo University of Science identify machine learning techniques that can help automate RHEED data analysis. Their findings could greatly accelerate semiconductor research and pave the way for faster, energy efficient electronic devices.

    The semiconductor industry has been growing steadily ever since its first steps in the mid-twentieth century and, thanks to the high-speed information and communication technologies it enabled, it has given way to the rapid digitalization of society. Today, in line with a tight global energy demand, there is a growing need for faster, more integrated, and more energy-efficient semiconductor devices.

    However, modern semiconductor processes have already reached the nanometer scale, and the design of novel high-performance materials now involves the structural analysis of semiconductor nanofilms. Reflection high-energy electron diffraction (RHEED) is a widely used analytical method for this purpose. RHEED can be used to determine the structures that form on the surface of thin films at the atomic level and can even capture structural changes in real time as the thin film is being synthesized!

    Unfortunately, for all its benefits, RHEED is sometimes hindered by the fact that its output patterns are complex and difficult to interpret. In virtually all cases, a highly skilled experimenter is needed to make sense of the huge amounts of data that RHEED can produce in the form of diffraction patterns. But what if we could make machine learning do most of the work when processing RHEED data?

    A team of researchers led by Dr. Naoka Nagamura, a visiting associate professor at Tokyo University of Science (TUS) and a senior researcher of National Institute for Materials Science (NIMS), Japan, has been working on just that. In their latest study, published online on 09 June 2022 in the international journal Science and Technology of Advanced Materials: Methods, the team explored the possibility of using machine learning to automatically analyze RHEED data. This work, which was supported by JST-PRESTO and JST-CREST, was the result of joint research by TUS and NIMS, Japan. It was co-authored by Ms. Asako Yoshinari, Prof. Masato Kotsugi also from TUS, and Dr. Yuma Iwasaki from NIMS.

    The researchers focused on the surface superstructures that form on the first atomic layers of clean single-crystal silicon (one of the most versatile semiconductor materials). depending on the amount of indium atoms adsorbed and slight differences in temperature. Surface superstructures are atomic arrangements unique to crystal surfaces where atoms stabilize in different periodic patterns than those inside the bulk of the crystal, depending on differences in the surrounding environment. Because they often exhibit unique physical properties, surface superstructures are the focus of much interest in materials science.

    First, the team used different hierarchical clustering methods, which are aimed at dividing samples into different clusters based on various measures of similarity. This approach serves to detect how many different surface superstructures are present. After trying different techniques, the researchers found that Ward’s method could best track the actual phase transitions in surface superstructures.

    The scientists then sought to determine the optimal process conditions for synthesizing each of the identified surface superstructures. They focused on the indium deposition time for which each superstructure was most extensively formed. Principal component analysis and other typical methods for dimensionality reduction did not perform well. Fortunately, non-negative matrix factorization, a different clustering and dimensionality reduction technique, could accurately and automatically obtain the optimal deposition times for each superstructure. Excited about these results, Dr. Nagamura remarks, “Our efforts will help automate the work that typically requires time-consuming manual analysis by specialists. We believe our study has the potential to change the way materials research is done and allow scientists to spend more time on creative pursuits.”

    Overall, the findings reported in this study will hopefully lead to new and effective ways of using machine learning technique for materials science—a central topic in the field of materials informatics. In turn, this would have implications in our everyday lives as existing devices and technologies are upgraded with better materials. “Our approach can be used to analyze the superstructures grown not only on thin-film silicon single-crystal surfaces, but also metal crystal surfaces, sapphire, silicon carbide, gallium nitride, and various other important substrates. Thus, we expect our work to accelerate the research and development of next-generation semiconductors and high-speed communication devices,” concludes Dr. Nagamura.

    We certainly hope to see more such discoveries in the future that can automate complex data analysis and ease the workload of scientists!

    ***

    Reference

    Title of original paper: Skill-agnostic analysis of reflection high-energy electron diffraction patterns for Si(111) surface superstructures using machine learning

    Journal: Science and Technology of Advanced Materials: Methods

    DOI: https://doi.org/10.1080/27660400.2022.2079942

    About The Tokyo University of Science

    Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

    With a mission of “Creating science and technology for the harmonious development of nature, human beings, and society”, TUS has undertaken a wide range of research from basic to applied science. TUS has embraced a multidisciplinary approach to research and undertaken intensive study in some of today’s most vital fields. TUS is a meritocracy where the best in science is recognized and nurtured. It is the only private university in Japan that has produced a Nobel Prize winner and the only private university in Asia to produce Nobel Prize winners within the natural sciences field.

    Website: https://www.tus.ac.jp/en/mediarelations/

    About Professor Masato Kotsugi from Tokyo University of Science

    Dr. Masato Kotsugi graduated from Sophia University, Japan, in 1996 and then received a PhD from the Graduate School of Engineering Science at Osaka University in 2001. He joined the Tokyo University of Science in 2015 as a lecturer and became a full Professor in the Department of Materials Creation Engineering in 2021. Prof. Kotsugi and students at his laboratory conduct cutting edge research on high-performance materials with the aim of creating a green energy society. He has published over 110 refereed papers and is currently interested in solid-state physics, magnetism, synchrotron radiation, and materials informatics.

    About Dr. Naoka Nagamura from National Institute for Materials Science

    Dr. Naoka Nagamura is a visiting Associate Professor at Tokyo University of Science, Japan and a senior researcher at the Research Center for Advanced Measurement and Characterization at National Institute for Materials Science, Japan. She obtained her Ph.D. from the University of Tokyo, Japan in 2011 and did a postdoctoral stint there from 2011–2013. Her research interests include graphene, synchrotron radiation X-ray analysis, operando analysis, imaging, photoemission spectroscopy, and surface and interface analysis. She has published 34 papers so far with over 500 citations to her credit.

    Funding information

    This study was supported by JSPS KAKENHI Grant No. 19H02561; JST-CREST Grant No. JPMJCR21O1; and JST-PRESTO Grant Nos. JPMJPR20T7 and JPMJPR17NB.

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