  s miniaturization of electrical and mechanical components used in modern
technology progresses, there is an increasing need for high-throughput and
low-cost micro-scale assembly techniques. Many current micro-assembly methods
are serial in nature, resulting in unfeasibly low throughput. Additionally, the
need for increasingly smaller tools to pick and place individual microparts
makes these methods cost prohibitive. Alternatively, parallel self-assembly or
directed-assembly techniques can be employed by utilizing forces dominant at
the micro and nano scales such as electro-kinetic, thermal, and capillary
forces. However, these forces are governed by complex equations and often act
on microparts simultaneously and competitively, making modeling and simulation
difficult. The research in this paper presents a novel phenomenological
approach to directed micro-assembly through the use of artificial intelligence
to correlate micro-particle movement via dielectrophoretic and electro-osmotic
forces in response to varying frequency of an applied non-uniform electric
field. This research serves as a proof of concept of the application of
artificial intelligence to create high yield low-cost micro-assembly
techniques, which will prove useful in a variety of fields including
micro-electrical-mechanical systems (MEMS), biotechnology, and tissue
engineering.
