Project Grant 2535791

The National Science Foundation Division of Electrical, Communications and Cyber Systems awarded Trustees of Dartmouth College $485,757 on October 15, 2026, under the Engineering program (CFDA 47.041) to develop a high-throughput semiconductor device platform accelerating material-process-device co-design through automated experimentation, machine learning, and electrostatic modeling.

The project combines automated experiments with physics-informed machine learning to efficiently explore large design spaces and identify material and processing conditions that produce optimal device performance, stability, and reliability. The experimental work focuses on printable metal oxide materials and solution-processed thin-film transistors fabricated via high-speed flexographic printing to generate large libraries with controlled variation in composition, thickness, and multilayer architecture. High-throughput electrical and optical measurements will characterize device performance. The platform targets semiconductor development for scaled devices, back-end-of-line integrated technologies, sensors, displays, and flexible electronics. Machine learning models will pinpoint origins of device variability and identify defect-related instabilities, while the project develops design principles transferable across semiconductor manufacturing platforms. The project also incorporates student training at multiple levels and integrates research into undergraduate teaching and mentoring to support future semiconductor workforce development.

Place of performance is Hanover, New Hampshire. The period of performance runs through September 30, 2029. Dartmouth College receives the award through its Office of Sponsored Projects Division.