The accelerated discovery cycle will allow scientists to aggregate and analyze known information about photoresist chemicals and materials from patents and the public literature. The use of this knowledge will drive modelling on traditional, high-performance computing systems and, in the future, on quantum computers. The combined results will be used to build AI models that automatically suggest new classes of compounds that meet specific efficiency and environmental targets. The most promising of these can then be tested experimentally with robotic systems, which can synthesize these molecular candidates with little human intervention.
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The work won’t be easy. Materials science and chemistry are among the most challenging fields of research, and the materials used in semiconductor manufacturing are complex and require many components to interact in very specific ways. The traditional trial-and-error method of seeking the right combinations of compounds and materials is too time-consuming and prohibitively expensive.
That’s why, over the next five years, scientists will embrace a new approach to materials design that enables the tech industry to more quickly produce sustainable materials for the production of semiconductors and electronic devices. That work could further help other manufacturers develop new, higher performance, yet safer and more environmentally-preferable materials to build products of all kinds.