Machine learning algorithms are revolutionizing many aspects of human endeavor such as business analytics, social media interactions, internet of things (IoT), autonomous navigation and healthcare. However, the computing platforms that are employed today to analyze, learn and infer insights from the big data streams associated with most of these applications are not suited for efficient real-time analysis and consumes significant amounts of power and computational effort. This has motivated the exploration of new devices based on novel materials that will enable area and power efficient computational systems that are more naturally suited for these applications. The goal of this symposium is to provide an overview of current active topics of research in materials science targeted at developing new nanoscale devices for machine learning and neuromorphic computing. The symposium will feature invited abstracts from researchers that will present high-level perspectives on machine learning trends and system-level requirements as well as cutting-edge developments on materials for these applications based on chalcogenides, metal-oxides, magnetic materials, ferroelectric materials etc.

atomic layer deposition chemical vapor deposition (CVD) (deposition) dielectric ferroelectric ferromagnetic nanoscale nucleation & growth simulation spintronic sputtering