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About this Abstract
Meeting	MS&T21: Materials Science & Technology
Symposium	Accelerating Materials Science with Big Data and Machine Learning
Presentation Title	A Data-driven Simulator for High-throughput Prediction of Electromigration-mediated Damage in Polycrystalline Interconnects
Author(s)	Peichen Wu, William Farmer, Kumar Ankit
On-Site Speaker (Planned)	Peichen Wu
Abstract Scope	Electromigration (EM) induced diffusional transport of metal atoms, which manifest as grain-boundary slits and voids in the metal line, often result in failure of an entire electronic component. Formulating preventive strategies and their efficient implementation involves the analysis of failure mechanisms in 4D microstructures via tedious in situ X-Ray tomography characterization as well as large-scale phase-field simulations, both of which are resource-intensive. Here, we present a data-driven simulation (DDS) technique, which for the first-time couples Artificial Neural Networks with microstructure modeling, to enable a high-throughput and an accurate prediction of defects’ evolution in progressively degrading interconnects. Our approach for validating the DDS-predicted EM failure rates by leveraging existing 4D datasets for a range of surface and grain boundary energies, crystal structure, grain texture, and electrical conductivity, as well as process parameters that include current density and temperature, will be discussed.

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