SNM: Robust Scalable Nanomanufacturing of Photonic Structures

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This project addresses the fundamental challenges in developing the low-cost and highly scalable nanomanufacturing solutions. The mainstream nanofabrication technologies often require the precise placement of materials at nanometer scale accuracy and thus, can be rather slow and expensive. On the other hand, self-assembly process of nanoparticles allows for spontaneous formation of large-scale nanostructures into certain patterns of configurations to achieve desired functions at very fast speed and low cost. In fact, this is the strategy being adopted by the nature in creating the fascinating structural coloration in many living creatures, such as one found in the birds' feathers. Taking the inspiration from the Nature, we will develop a new design methodology in enabling scalable nanomanufacturing process using the low-cost self-assembly process while simultaneously develop the strategy to control and mitigate the manufacturing defects. The success of this project will improve the robustness of scalable nanomanufacturing process and products and therefore accelerate the transformation from nanotechnology to a broad range of profitable commercial products. The emphasis on the solar energy will have a notable positive impact on developing green, alternative energy suppliers. This project will also establish a wide range of dissemination and outreach programs, with particular focus on the new hands-on teaching module to attract the attention of students from underrepresented groups to engineering careers and research opportunities. The intellectual significance of this project to develop a novel robust scalable nanomanufacturing methodology that integrates: 1) non-deterministic design representations that exploit the stochastic nature of bottom-up nanomanufacturing processes; 2) a manufacturing-aware physical modeling strategy that facilitates a direct process-structure-performance data pipeline; 3) a concurrent and robust design framework that allows simultaneous optimization of the structure and process variables; and ultimately 4) nanometrology assisted defect detection and control that combines statistical process control, stochastic model correction, and robust design for a complete suite of cost-effective and robust scalable nanomanufacturing solutions. The methodology will be validated using two testbeds: a) low-cost self-assembled block-copolymer light-trapping coating for thin film solar cells, and b) smart-window coating through hierarchical assembly of nanocomposite materials. A wide range of dissemination and outreach programs will be established, with particular focus on the new hands-on teaching module to attract the attention of students from under-represented groups to engineering careers and research opportunities. The work offers unique research and educational experiences for researchers across the fields of design, nano-engineering, and industrial statistics, and will train students in an interdisciplinary learning environment.