{"title":"Modeling of capillary forces and binding sites for fluidic self-assembly","authors":"K. Böhringer, U. Srinivasan, Roger T. Howe","doi":"10.1109/MEMSYS.2001.906555","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":311365,"journal":{"name":"Technical Digest. MEMS 2001. 14th IEEE International Conference on Micro Electro Mechanical Systems (Cat. No.01CH37090)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"93","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technical Digest. MEMS 2001. 14th IEEE International Conference on Micro Electro Mechanical Systems (Cat. No.01CH37090)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MEMSYS.2001.906555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 93
Abstract
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.