The research objective of this award is to establish statistics-transformed nanostructure growth process models and efficient experimental strategies for improving process repeatability in the fabrication of nanostructures for the application in photovoltaic cells. To achieve repeatable fabrication of photovoltaic cells with respect to yield (productivity) and uniformity (quality), it is essential to identify and optimize the growth conditions rooted on predictive process models. These models will capture the mechanisms of nanostructure growth under process uncertainties. Since most of the current growth kinetics models are deterministic, the research will first devise statistics-transformed nanostructure growth process models that account for uncertainties. Based on the process model, optimal experimental strategies will be established for model estimation and validation with a high degree of precision under cost and time constraints. The methodology will be validated through controlled growth of nanowires and fabrication of photovoltaic cells.
Successful completion of the proposed research will lead to new tools and methods for improving process repeatability and yield in nanomanufacturing, particularly in the large scale fabrication of photovaic cells. Reducing cost in photovoltaics gives prospect of achieving highly efficient and low-cost solar energy conversion, increasing the utilization of clean and renewable energy, and creating green job opportunities. This truly interdisciplinary project will promote training of a new breed of workforce excelling at nanomanufacturing process modeling and optimization and contributing to the sustainable growth of US economy. The educational goal will be achieved through (1) creating interdisciplinary nanomanufacturing curricular materials, (2) enhancing the existing research and education collaborations between University of Southern California and Harvard University, and (3) involving women/minority students through REU (Research Experience for Undergraduates) program. The generated knowledge will be broadly disseminated through leading journals, conferences, websites, and collaborators.
The objective of NSF project CMMI-1000972 is to establish nanostructure growth process models and efficient experimental strategies for improving process repeatability in the fabrication of nanostructures for the application in photovoltaic cells. As a one-dimensional nanostructure, nanowire has approved to be an effective candidate structure material for photovoltaics to efficiently harvest solar energy. For large-scale production of nanowire-based photovoltaics, uniformity in nanowire structure is essential for the efficiency of energy conversion. The outcomes of this project are therefore mainly related to the prediction and control of nanowire fabrication processes for the purpose of uniformity control. The prediction of nanowire fabrication is accomplished in the following three aspects: At macroscopic level, we successfully build a functional relationship between nanowire geometric features such as height with process parameters including patterning diameter and pitch, spatial location of nanowires, and growth time. Studies on real experimental data confirm that our model accurately explains both spatial and temporal growth patterns of nanowires. In addition, by establishing the relationship between substrate edge dimension with nanowire uniformity, the uniformity control can be improved through adjusting edge area dimension. At microscopic level, we achieve an understanding of the interaction effect of neighboring nanostructures on the structure uniformity. Nanostructure local features and variability are characterized and related to nanostructure interactions, a major contributor to structure and defect formation. The achieved understanding enable us to develop a defect detection method for uniformity control. We also develop metrics and methods to characterize nanostructure interactions and link the interactions with structure variability. Experimental investigation leads to GaAs nanowire solar cells forming junctions in axial junction which may enable the attainment of high open circuit voltage and integration into multi-junction solar cells. The approach opens up great opportunity for future low-cost, high-efficiency photovoltaics. These research outcomes contribute to the knowledge base of manufacturing nanostructured photovoltaics for efficient energy conversion. Education materials have been developed to train interdisciplinary workforce. The outcomes are broadly disseminated through research publications and publicity of winning best paper award.
