Massively parallel self-assembly is emerging as an efficient, low-cost alternative to conventional pick-and-place assembly of microfabricated components. The fluidic self-assembly technique we have developed exploits hydrophobic-hydrophilic surface patterning and capillary forces of an adhesive liquid between binding sites to drive the assembly process. To achieve high alignment yield, the desired assembly configuration must be a (global) energy minimum, while other (local) energy minima corresponding to undesired configurations should be avoided. Thus, the design of an effective fluidic self-assembly system using this technique requires an understanding of the interfacial phenomena involved in capillary forces; improvement of its performance involves the global optimization of design parameters such as binding site shapes and surface chemistry. This paper presents a model and computational tools for the efficient analysis and simulation of fluidic self-assembly. The strong, close range attractive forces that govern our fluidic self-assembly technique are approximated by a purely geometric model, which allows the application of efficient algorithms to predict system behavior. Various binding site designs are analyzed, and the results are compared with experimental observations. For a given binding site design, the model predicts the outcome of the self-assembly process by determining minimum energy configurations and detecting unwanted local minima, thus estimating expected yield. These results can be employed toward the design of more efficient self-assembly systems.
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