UC Irvine

Assembly of microdevices from constituent parts usually relies on serial steps via assembly processes such as pick and place operations. These serial assembly processes are slow and produce insufficient yield as parts size decreases from millimeters to microns. The present work introduces an electrokinetic assembly process that acts on micro- and nano-parts via a guided, noncontact, scalable process capable of selectively attracting specific types of microparts by varying the frequency and potential of the applied AC signal. The Photolithography and Carbon-MEMs (CMEMs) processes are utilized in creating interdigitated electrode arrays (IDEAs) that are used as substrates for the discussed electrokinetic guided micro and nano assembly. Electrokinetic forces under consideration include dielectrophoresis (DEP) and electroosmosis (EO). The work starts with outlining the current state-of-the-art in the field of micro- and nano-assembly and progresses to describe the fabrication and experimental setup of the electrokinetic assembly platform. The IDEAs are coated with a layer of lithographically patterned resist so that when an AC electric field is applied to the IDEA, microparticles suspended in the aqueous solution are attracted to the open regions of the electrodes not covered by photoresist. The interplay between AC electro-osmosis and dielectrophoretic forces guides polystyrene beads of different sizes to assemble in regions, or “wells,” uncovered by photoresists atop the electrodes. This is followed by the results and discussion of the electrokinetic assembly of 1 micron and 5 micron polymer (polystyrene) beads at specific locations on glassy carbon interdigitated electrode arrays. One application proposed for this microassembly technique is the post-amplification of chemical and biological assays by collecting the fluorescent beads into the wells for enhancement of the fluorescent signal of the test. The work subsequently introduces an Artificial Intelligence (AI) based approach that supplements the electrokinetic handling of the microbeads. The visual feed from the digital camera is digitally processed to recognize the interfaces of the beads, and the AI algorithms are then used to determine if the beads are attracted to or are repelled from the electrodes. This process is used to automatically determine the dielectrophoretic cross-over frequency, a critical property for studying dielectrophoresis of micro- and nano- parts. In this study, a Feedback Control System first uses a digital camera and a microscope to capture microbeads' motion. And then, the OpenCV software package analyzes the relative positions of microbeads in consecutive frames to determine the direction of the microbeads’ movement for the characterization of frequency ranges for positive and negative DEP. Finally, a step-wise process using DEP force is presented. This step-wise process is used to deposit the carbon nanotube bridges along the applied electric field lines between two neighboring electrodes, and its application on healing damaged microelectrodes and the performance of the healed microelectrode are discussed.