Evolutionary controllers for identifying viable regimes and obtaining optimal performance in precision inkjet systems

Drop on demand piezoelectric inkjets have become an important device for direct patterning and adaptive material deposition in micro- and nano-fabrication applications. Key performance, reliability, and cost metrics for piezo-jets include drop volume minimization, drop volume accuracy and precision, drop placement accuracy, drop velocity, hours of continuous operation, and throughput. Accurately predicting drop formation from an actuation waveform using physics-based models is challenging as such models require knowledge of several inkjet parameters that cannot be determined non-destructively. Current practice involves ad-hoc manual recalibration of the actuation waveform to obtain reliable jetting of a variety of materials. This prevents the use of higher order waveforms defined by large numbers of parameters, where such waveforms have been demonstrated in the literature to achieve more aggressive performance metrics than lower order waveforms. This dissertation presents automatic piezo-jet waveform tuning methods based on evolutionary algorithms and computer-vision-based monitoring drops in-flight and drops that have been dispensed onto a substrate. Actuating and monitoring a piezo-jet as part of a “machine-in-the-loop” optimization scheme circumvents the need for complex forward models, as key performance metrics are estimated from images of actual jetted drops. Automatic tuning also enables exploration of previously unachievable highly complicated higher order waveforms comprised of more than a hundred parameters. In this dissertation, three fixed waveform topologies of increasing complexity were applied to optimize waveforms using genetic algorithms (GA) for a single-nozzle inkjet based on computer vision feedback from in-flight drop monitoring. These GA experiments automatically found waveforms for water and ethyl acetate, wherein the latter is considered rheologically impossible to jet based on fluid mechanics studies in the literature, but which was jetted at drop volumes measuring 0.8 pL continuously for several hours without faults. The resulting ratio between the 11.5 µm drop diameter and the 50 µm inkjet nozzle aperture was an impressive 23%. Next, a novel optimization scheme of a GA with a variable-length or “free” topology was developed to optimize waveforms for a more complex multi-nozzle piezo-jet using top-down imaging of drops dispensed on a silicon wafer to measure performance. This free-topology GA enabled exploration of highly sophisticated controllers and resulted in waveforms with as many as 124 parameters that reduced drop volume by 22.8% to an estimated 336 fL as compared to the waveforms found by a fixed-topology GA with 13 parameters.