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s miniaturization of electrical and mechanical components used in moderntechnology progresses, there is an increasing need for high-throughput andlow-cost micro-scale assembly techniques. Many current micro-assembly methodsare serial in nature, resulting in unfeasibly low throughput. Additionally, theneed for increasingly smaller tools to pick and place individual micropartsmakes these methods cost prohibitive. Alternatively, parallel self-assembly ordirected-assembly techniques can be employed by utilizing forces dominant atthe micro and nano scales such as electro-kinetic, thermal, and capillaryforces. However, these forces are governed by complex equations and often acton microparts simultaneously and competitively, making modeling and simulationdifficult. The research in this paper presents a novel phenomenologicalapproach to directed micro-assembly through the use of artificial intelligenceto correlate micro-particle movement via dielectrophoretic and electro-osmoticforces in response to varying frequency of an applied non-uniform electricfield. This research serves as a proof of concept of the application ofartificial intelligence to create high yield low-cost micro-assemblytechniques, which will prove useful in a variety of fields includingmicro-electrical-mechanical systems (MEMS), biotechnology, and tissueengineering.

Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence (AI) Guided Microassembly