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Meeting MS&T23: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Atomistic Perspectives in Characterizing Crystalline Defect Formation in Amorphous Silicon Nitride
Author(s) Tesia Janicki, Carlos Chacon, Edwin Chiu, Jason Gibson, Scott Grutzik, Khalid Hattar, Richard Hennig, Hojun Lim, Calvin Parkin, Jennie Podlevsky, Aashique Rezwan, Chris Bishop, J. Matthew D. Lane
On-Site Speaker (Planned) Tesia Janicki
Abstract Scope Amorphous silicon nitride (a-SiN) is used as a low-stress, layered component in microelectronics devices. Spurious crystallite formation in the a-SiN layer during fabrication distorts electronic properties. We introduce a multiscale effort to identify the root causes and mechanisms of crystallite formation with the goal of adapting synthesis processes to prevent this spurious crystallization. Our presentation focuses on atomistic-scale models, invoking molecular dynamics with a series of empirical interatomic potentials to compare and predict structural, thermodynamic, and kinetic properties. Atomistic results are discussed alongside experimental observations and integration with mesoscale models towards an improved understanding of crystallization in a-SiN. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A B-C Story, Investigated by A.I. and CALPHAD
An ICME Approach for Short Fiber Reinforced Ceramic Matrix Composite via Direct Ink Writing
Atomistic Perspectives in Characterizing Crystalline Defect Formation in Amorphous Silicon Nitride
Combining Experimental and Simulation Datasets in Machine Learning for Glass Properties Prediction
Comparison of Core Level Chemical Shift in CH3NH3PbBr3 Perovskite Due to Surface Terminations and Orientations of CH3NH3 Ion
D-10: Unraveling the structure and mechanical properties ZIFs and its topological equivalents: Large scale simulations
D-9: Discrete Element Simulation of Delamination in Thermal Barrier Coating
Decoding the Structural Genome of Silicate Glasses
Defect Chemistry and Electrical Properties of Doped BaTiO3
Development of a Machine Learned Interatomic Potential for Shock Simulations of Boron Carbide
First-Principles Modeling of Thermodynamics and Kinetics of Thin-Film Tungsten Carbides
Fracture Resistance of Rare-earth Phosphates as Environmental Barrier Coatings under CMAS Corrosion
Generation of Spectral Neighbor Analysis Potentials for Alpha Boron and Comparison of the Results with the Angular Dependent Potential
Lithium Dopant and Surface Effects on the Band Gap of Calcium Hexaboride (CaB6) Using DFT Methods
Machine Learning Prediction of Heat Capacity for Solid Mixtures of Pseudo-binary Oxides
Using Deep Learning to Develop a Smart and Sustainable Cement Manufacturing Process

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