An experimental investigation for parametric appraisal of electrohydrodynamic-driven microfabrication approach using teaching and learning-based optimization

Abstract

Electrohydrodynamic (EHD)-based microfabrication process is a novel high-resolution pattern generation technology for micro/nanoscale system fabrication. Achieving tiny droplets during deposition has been a critical issue for the further development of this new emerging technology. It is imperative to identify the proper fabrication parameters to get the desired resolution operation. In the current study, first, an experimental EHD microfabrication setup is developed, and then experiments are carried out to search for an operating condition, which can promote high-resolution droplet generation. The experiments are planned by design of experiment scheme. The influence of the operating control parameters such as nozzle to substrate gap, voltage and the supply rate of the material on deposited droplets diameter is investigated in the current work. Characterization study reveals that nozzle to substrate gap and supply rate have a positive effect on deposition behavior, whereas voltage has a negative association with the droplet resolution. For better control of the EHD process, a causal relationship is developed between process control and performance parameter. A metaheuristic search algorithm named teaching and learning-based optimization (TLBO) is employed to find out the optimal operating environment of the EHD process. The results are compared with the traditional robust parameter design approach, and the authors found that TLBO performs more effectively for resolution improvement in the EHD-driven microfabrication process. Moreover, the sequence in which the control parameters govern the fabrication process is identified and reported.