ABSTRACT
The idea of making “nanostructures” that are composed of just one or a few atoms has great appeal, both as a scientific challenge and for practical reasons. In recent years, scientists have learned various techniques for building nanostructures, but they have only just begun to investigate these structures’ properties and potential applications (Scientific American, 2002). In a nanoscale environment, the effects of design parameters on product characteristics usually cannot be known purely from phenomenological models; therefore, nanotechnology design often requires data collection and analysis. There is a lot of excellent nanoscience in the literature and in research labs, but there is a gap between nanoscience and nanotechnology. Often, the experimentation used to build up the background of nanoscience, while methodical (e.g., changing one key factor at a time while keeping others constant), is not efficient. Through our interdisciplinary collaborations, we have identified the importance of statistically based design of experiments (DOE) to the research and development of nanomanufacturing. Drawing from these interactions as well as from the literature, this chapter addresses three cases that closely link DOE with the needs of nanomanufacturing.
