Redefining Materials Innovation in the Age of AI and Digital Sciences
As the industry advances toward CFET transistor architectures, 500+ layer 3D NAND, 3D DRAM, and angstrom-level critical interconnect dimensions, device manufacturers are increasingly reliant on disruptive materials to achieve the density and performance gains required at each successive technology node. The traditional approach to materials discovery and qualification, relying on sequential experimentation and empirical trial-and-error, can no longer keep pace with the velocity of technology roadmap demands.
This talk presents a framework for transforming materials development through the convergence of artificial intelligence (AI), modeling and simulation, and digital sciences; enabling a more predictive, connected, and accelerated approach to materials innovation. Drawing on real-world case studies, we illustrate how virtual engineering, multiphysics modeling and simulation, computational chemistry, and digital thread applications are accelerating development cycles while dramatically reducing experimental iterations, establishing a new operating model for materials innovation in the AI era.
