Technology Presentation 1

Dr. Noritaka USAMI, Ph.D.

Professor, Nagoya University

Graduate School of Engineering
Institute of Materials and Systems for Sustainability

Center for Integrated Research of Future Electronics

Nagoya University

Multicrystalline informatics for development of high-performance materials


We report on our recent attempt to pioneer “multicrystalline informatics” through collaboration of experiments, theory, computation, and machine learning to establish universal guidelines how we can obtain high-performance multicrystalline materials. We employ silicon as a model material, and develop various useful machine learning models. One example is a neural network to predict distribution of crystal orientations in a large-area sample from multiple optical images. Transfer learning of pre-trained image classifier could predict spatial distribution of probability of dislocations generation from photoluminescence images. Extracted regions with high probability of dislocations generation could be characterized by multiscale experiments as well as computation using artificial-neural-network interatomic potential to disclose the physics behind. The obtained knowledge could be useful for process development of high-performance multicrystalline materials.


Keywords: multicrystalline informatics, machine learning, dislocations, photoluminescence


Technology Presentation 2

Dr. Douglas L. IRVING, Ph.D.

Professor, North Carolina State University

Department of Materials Science and Engineering

Point defect informatics: Identifying routes to realize targeted properties of electronic materials

Computational and informatics approaches are emerging as next generation tools for materials discovery and design. To date there has been a significant focus on the identification of new materials and new compounds with a desired set of properties. Defects are often neglected in this initial screening even though they are an equally important aspect governing the functional properties of real materials. For example, in semiconductors and dielectrics, the electrical properties are determined by the impurities and native point defects in the material. Well characterized impurities can be intentionally introduced to cause n- and p-type conductivity. While this doping has become routine in silicon, challenges remain to tailoring the electrical and optical properties of wider bandgap materials. In these systems, unintentional defects can lead to less desirable outcomes and routes around these defects require the development of a mechanistic understanding. In this talk, I will highlight the use of first principles methods, point defect informatics, artificial intelligence, and grand canonical defect chemistry as means to tailor the electrical and optical properties of functional semiconductors and insulators from the bottom up.[1]–[9] I will highlight how these simulation tools have been integrated with parallel experimental synthesis and characterization efforts.

Keywords: Informatics, semiconductors, point defects, electrical and optical properties, defect equilibria


Technology Presentation 3

Dr. Toru UJIHARA, Ph.D.

Professor, Nagoya University

Institute of Materials and Systems for Sustainability

Center for Integrated Research of Future Electronics Innovative Devices Section

Title "TBD"