Nanomanufacturing (NM) is the utilization of bottom-up directed assembly or top-down high resolution processing to control matter at the nanoscale in one, two, and three dimensions for reproducible, commercial-scale production.  It can generate profound impacts on health, wealth, and national security. Full-scale NM, however, requires a new set of quality control methodology and tools to ensure throughput, yield, and quality. This CAREER project aims to create a body of knowledge regarding the system-level modeling and control of process variations in NM; and an education curricula to educate a new generation of NM engineers with a concentration on quality control.

The research outcomes regarding quality control methods involve the following aspects:

• Scalable and robust process modeling under uncertainties: Scalable NM models ensure model validity when a process is scaled up by size, quantity, throughput or yield; and ensure robustness of process outputs when a process operates under uncertainties. We establish modeling methods to achieve both scalability and robustness. 
• Scale-up process modeling and experimental design for processes across multiple physical domains: NM processes can operate under different physical domains, which results in complications in experimental investigation and modeling. To predict behaviors of NM processes under different domains, we devise an efficient experimental design and modeling approaches by identifying model structures in low-dimensional space.  
• 2D nanostructure growth process modeling and control: Nanomaterials such as graphene represents an exciting new class of 2D nanomaterials. We established a novel stochastic modeling approach for controllable manufacturing of 2D nanostructures under uncertainties.
• A new modeling methodology for NM process modeling under large uncertainties: We devised a cross-domain model building and validation (CDMV) method, a modeling strategy beyond the existing physical, statistical, and physical-statistical modeling methods, to accommodate NM scenarios characterized by extremely limited data and physical knowledge.

The developed methodologies cover experimental design for scalable NM experimentation and data collection and scale-up process modeling under various uncertainties. The methodologies are applicable to manufacturing of zero-dimensional (0D), 1D, and 2D nanomaterials.

Regarding educational outcomes, a curriculum of integrated NM and nanoinformatics has been developed with a new course added into existing curricula. Three PhD students were partially supported on the project and two of them graduated with dissertation topics on nanomanufacturing. The project provides opportunities of interdisciplinary training for PhD students in Industrial Engineering, Nanoscience, and Statistics. PhD students have landed successful careers after graduation.

With the grant support, a Nano Quality Control Lab has been established with addition of advanced equipment for research and education. Outreach to K-5 students was conducted by introducing advanced sensing equipment and concept in science fair.

National and international collaborations have been enhanced for nanomanufacturing research with partners from Harvard University, Georgia Tech, University of Tennessee, Florida International University, Hong Kong University of Science and Technology, and Tsinghua University. Research outcomes have disseminated nationally and internationally. 

Last Modified: 10/04/2017
Modified by: Qiang Huang