Technical Description: This project seeks to develop the foundations for a new framework for describing the processing of complex materials systems. It is being achieved by integrating computation, experimental measurements, simulation and data sets into a predictive descriptor of kinetic evolution during materials processing. The specific focus / example is the development of predictive simulation of the structural, electronic and optical properties of dislocation networks in epitaxial films, with an ultimate goal of accelerating the development of device-quality material in such systems. The project is first extending an existing simulator developed for predicting dislocation generation in the GeSi/Si system during growth and/or thermal processing. Phylogenetic mapping methods, developed for the fields of evolutionary biology and bioinformatics, are employed to refine materials parameters and fundamental mechanistic assumptions in the simulator by optimizing the matching of experimental and simulated data sets. An initial important goal is to define a "minimum data set" necessary to adequately describe the energetic and kinetic parameters of the evolving system, and enable quantitative predictions. Next, the project is extending these methods to a new materials system, with significantly different dislocation generation mechanisms, strained III-nitride films. This enables both assessment of the generality of the simulator method, and should advance understanding of dislocation generation in the III-nitride systems. Finally, the simulator is being extended to enable prediction of optical and electronic parameters that correlate to the observed /predicted defect densities. This involves first-principle calculations of electronic properties of specific defect configurations, enabling an additional generation of simulator that correlates predicted defect densities and consequent (opto)electronic activity to observed optoelectronic and device properties of the dislocated material.
Non-Technical Description: This project is developing new frameworks for enabling predictive simulation of defect generation during growth and processing of thin-film crystalline materials, and the effects of these defects upon the performance parameters of these materials. This enables accelerated device development cycle times by predicting conditions for device-quality materials synthesis ahead of full processing cycles. This ultimately contributes to the national need to accelerate the transition from new materials discoveries to manufacturable technologies. The method to be developed employs the comparison of predictions from the simulator to extensive experimental data sets to refine and "train" the simulator. It both improves the predictive capability of the simulator for the given system in which it is trained, and accelerates transfer of its application to new materials systems. The set of students working on this project are being schooled in this vision of optimizing integration of calculation, experiment and data management, thus helping to establish a workforce trained in these methods. The research team is making the resultant process simulator accessible to the research and development community and is responding to selected requests for specific updates or enhancements.
This project is co-funded by the Electronic and Photonic Materials Program (EPM) and the Computational and Data driven Materials Research Program (CDMR), both in the Division of Materials Research (DMR).
