Modeling and analysis of nanostructure growth process kinetics and variations for scalable nanomanufacturing

Nanomanufacturing is currently a major bottleneck that hinders the transformation of nanotechnology from laboratory to industrial applications. Due to both limited process understanding and control, there are great challenges for reliable and cost-effective scalable production of nanostructures. As a result, despite of nanomaterials' superior electrical, mechanical, chemical and biological properties, their great potentials in high impact fields such as energy, medicine and information technology have not been materialized.;In order to achieve scalable nanomanufacturing, the nanostructure fabrication process has to be quantitatively modeled before subsequent improvement efforts such as diagnosis, monitoring and control. This research will therefore focuses on characterizing nanostructure growth processes to understand growth kinetics which is the law governing how growth behaviors change with time. This dissertation will also investigate the among experimental runs variations and identify their root causes for improved process modeling.;There are two fundamental research challenges in nanomanufacturing process modeling: (i) There are only limited physical knowledge for nanostructure growth processes. Despite of numerous studies in nanostructures growth area, we are still often largely uncertain about the physical mechanisms driven the growth process under a certain condition. The complexity comes from the fact that there are often multiple mechanisms at work in one growth process and it is very difficult to identify their relative contributions. (ii) Experimental observations are expensive and time-consuming to obtain. Nanostructure growth experiments are usually costly due to expensive equipment (such as metal-organic chemical vapor deposition (MOCVD) system) as well as requirement of constant human attention. Moreover, the measurement of nanostructures often involves equipment such as scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscope (AFM). Besides significant equipment cost, one can only view/measure a tiny portion of the substrate at one time.;This dissertation will systematically model nanostructure growth process kinetics and its variation as part of greater effort towards achieving scalable nanomanufacturing. The focus of the modeling work is on quantitative macro scale measurements (such as overall weight) of nanostructure grown using "bottom-up" instead of "top-down" approach. Special emphasis is placed on integrating physical knowledge and experimental data due to the challenges mentioned above in the whole modeling process. The major research tasks includes: (i) statistical model building and selection to gain initial understanding of nanowire weight growth kinetics; (ii) cross-domain model building and validation(CDMV) to utilize all available information in both physical and statistical domains and achieve greater confidence in process modeling; (iii) characterization of variations among nano experimental runs under larger uncertainties. The first two tasks are more focused on modeling the general growth kinetics while the last one adds among-runs variations modeling to improve prediction as well as provide guidance for future process improvement. Observed nanowire weight growth data are used to demonstrate and validate proposed models.;In summary, this work tackles two central challenges in nanomanufacturing modeling, namely, uncertain physical mechanism and limited data, by developing a cross-domain modeling framework. The CDMV modeling framework developed here incorporates both physical knowledge and experimental observations and achieves high modeling confidence. Moreover, this modeling framework can be extended to characterize and identify potential variabilities sources and thus reduce the process uncertainties.;For future work, one potential extension is to incorporate inherent nanowire growth variation modeling. More specifically, instead of generalizing from one nanowire to the whole substrate and using it to represent the general growth trend, one can model each nanowire individually. With such approach, one can further investigate the growth variabilities over each substrate which is not possible with current macroscopic model.