Developing Data Standards for Accelerated Digital R&D of Semiconductor Materials from Nanoscale

Semiconductors electronics is integral to America’s economic and national security as manifested by the CHIPS act. Ever-increasing materials requirements for semiconductor R&D, combined with the advent of machine learning, and availability of materials databases, pose a need to understand and exploit these methods for acceleration of the materials and manufacturing lifecycle. The proposal aims to provide a digital framework establishing modular and flexible data standards allowing to accelerate semiconductor materials research. The proposed work will help researchers involved in the NIST-lead CHIPS and Materials Genome initiatives and many other materials researchers worldwide organize and accelerate their work for a critical set of applications of interest to both the public and private sector. The proposal elaborates on the progress demonstrated during Phase I to improve the speed and efficiency of the research and development R&D of new materials and chemicals and enable AI/ML data-driven capabilities that facilitate the development of new kinds of products for semiconductor electronics. The proposal benefits the customers and the US semiconductor materials R&D community by (1) enabling digital practices that are accessible, flexible, and materials-specific, (2) allowing to participate in collaborative research without revealing sensitive information, and (3) reducing the complexity, sparseness and heterogeneity of the available data. All of these benefits aim to accelerate the pace of new electronic materials development and advance the speed of semiconductor manufacturing.