We leverage advanced machine learning techniques to predict material properties, use adaptive learning for experiment design, and automate experimental synthesis and characterization enabling faster and more efficient material discovery and optimization.
Our research focuses on developing and optimizing amorphous oxide semiconductors and other n- and p-channel BEOL semiconductors for applications in 3D integrated memory and logic.
Our work in scaled interconnects aims to suppress surface and grain boundary scattering to outperform Cu and Ru interconnects at sub 400 nm2 dimensions.
We use machine learning guided theory and experiments for newt insulator-to-metal transition oxide materials, enabling advanced functionalities for selector devices and RF switches.
Our research investigates quantum defects, focusing on vacancies in SiC, for quantum interconnects and advanced sensing technologies.
We synthesize transition metal dichalcogenides to explore their electronic, optical, and magnetic properties.
