Ultrahigh Resolution Printing of Functional Optical and Electronics Materials as a Sustainable Approach for FHE and Metasurface Manufacturing

Advances in high-resolution electrohydrodynamic inkjet (EHD) printing of functional materials are enabling scalable manufacturing of flexible electronics and optical metasurfaces. These developments provide fully additive pathways for the fabrication of flexible optoelectronics that rivals and, in some cases, even exceed the performance of subtractive processing technologies. EHD moves additive printing of functional materials from conventional printing resolution limits of >20 microns down to the 1micron regime, matching the capabilities of large-area flat panel photolithography. EHD can also be applied to dielectric and optical materials at orders of magnitude lower cost and higher throughput than metasurface fabrication technologies such as e-beam lithography. The electric fields in EHD break through the surface energy barriers that prevent conventional inkjet from forming droplets smaller than 1pL and feature linewidths <20 microns. In EHD, 1000X smaller femtoliter droplets can be formed to print features <1 micron form a broader range of viscosities and functional materials. In this talk, we will discuss the University of Washington's progress in EHD processing, modeling, and ink formulation that have allowed for the fabrication of ultrahigh-resolution printed conductors for FHE and hypersonic applications printed quantum materials, and metasurfaces with drop-on-demand volume control. First, we will discuss the challenges involved in printing conducting traces with single-digit micron linewidth. Extensive exploration of a large printing variable parameter space including jetting frequency, voltage, and speed, and also the surface treatment and ink mass loading has been necessary to obtain good conductors. The workflow related to this task will be illustrated by experimental results from commercially available inks made for traditional piezoelectric inkjet printing. The latter will be used to illustrate the need for custom-made materials and advanced process control methods involving machine learning and advanced drop analysis in EHD.