ABSTRACT

Quantum material architectures consist of graphene and other two-dimensional materials, which, when stacked in precise three-dimensional architectures, exhibit unique and tunable mechanical, electrical, optical, and magnetic properties. These three-dimensional architectures have broad potential applications and are highly promising components for microchips, batteries, antennas, chemical and biological sensors, solar-cells and neural interfaces. However, currently, due to the lack of fundamental understanding of the physical and chemical processes, it has been difficult to control or scale the manufacturing of these three-dimensional structures. This Future Manufacturing (FM) grant is to develop a transformative Future Manufacturing platform for quantum material architectures using a cybermanufacturing approach, which combines artificial intelligence, robotics, multiscale modeling, and predictive simulation for the automated and parallel assembly of multiple two-dimensional materials into complex three-dimensional structures. This platform enables future production of high-quality, custom quantum material architectures for broad and critical applications, supporting continued U.S. leadership in technology development. The research in cybermanufacturing is integrated with innovative educational programs for cross-disciplinary training of scientists and engineers, especially, women and underrepresented minorities, in advanced manufacturing, artificial intelligence and quantum structures, as well as engaging the public in future manufacturing concepts.

This grant research focuses on a fundamentally new method for scalable manufacturing of 3D quantum material architectures or van der Waals heterostructures (vdWHs) using microfluidic assembly. vdWHs are composed of unlimited combinations of atomically thin layers and exhibit interesting emerging functionalities. The key process innovation is precision microfluidic folding of 2D materials, which has been demonstrated at a small-scale. This method has promising potential to scale up to wafer scale, with no fundamental limit on scaling. A second key innovation is embedding artificial intelligence (AI) across all aspects of the manufacturing process flow, from low-level precision control, to automated characterization, to high-level structure predictions. Predictive simulation and visualization tools combined with in situ spectroscopy allow real-time analysis of atomic-scale physical and chemical processes and their control. Moreover, parallel self-assembly in microfluidic environments is investigated as a pathway toward truly scalable manufacturing. The expected outcome of the award is to produce superlattices consisting of tens of atomic layers with precisely engineered stacking order and alignment, leading to fundamentally new custom quantum material architectures with electronic and photonic properties impossible to obtain from conventional material architectures. This research advances fundamental knowledge in material physics, nanoscale electronics and photonic science leading the way to manufacturing of future devices, such as twistronics. A key outcome is an AI-driven, robotics-controlled cybermanufacturing microfluidic platform that is capable of manufacturing complex structures for emerging quantum and other device applications.

This Future Manufacturing research grant is supported by the following Divisions in the Engineering Directorate: Civil, Mechanical and Manufacturing Innovation; Electrical, Communications and Cyber Systems; and Engineering Education and Centers; and the following Divisions in the Mathematical and Physical Sciences: Materials Research; Chemistry; and Mathematical Sciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: awardsearch@nsf.gov.