The objective of this research is to investigate the fundamental principles of self-assembly processes for the massively parallel manufacture of engineered systems at the micro to nano scale. The newly developed models will be able to describe and predict the relationship between system design parameters (e.g., materials, geometry, component count) and process performance parameters (e.g., assembly time, yield). These newfound theoretical results will be validated with an experimental "model system". The approach is a unique combination of theoretical, computational techniques, developed in parallel with MEMS laboratory experiments. Specific goals are the development of (a) computational models of the physics of part-to-part binding interactions that can predict characteristics such as binding range, strength, and alignment accuracy; (b) design synthesis tools, which in turn will lead to new or improved manufacturing methods based on engineered self-assembly; (c) novel binding mechanisms, and (d) analytical yet tractable models of large-scale self-assembly processes that can predict process parameters such as yield and expected assembly times. The first broader impact is the potential to revolutionize manufacturing, by introducing a paradigm shift away from serial, deterministic assembly automation to massively parallel, stochastic self-assembly. The second broader impact lies in the involvement of undergraduate and minority students in this cutting edge research, which has been shown to be a crucial part of a student's later decision to become an engineer or researcher. The PIs participate in three programs set up by the University of Washington, which facilitate the matching of highly motivated candidates from underrepresented groups to their research projects.
