The realization of a high-speed printing technique with high resolution and pattern fidelity is critical to making printed electronics a viable technology for electronics manufacturing. The printing requirements of printed electronics are substantially different that those of graphic arts. To make printed electronics a reality, it is necessary to deliver high resolution, good reproducibility, excellent pattern fidelity, high process throughput, and compatibility with the requisite semiconductor, dielectric, and conductor inks. In this paper, we review the physics of pattern formation from pixelated primitives, such as those that exist during inkjet and gravure printing, and will show how control of drop merging and drying can be used to produce high-fidelity shapes, including lines, squares, and intersections. We additionally discuss the physical underpinnings of gravure printing and inkjet printing, and show how these techniques can be scaled to produce high-fidelity highly scaled patterns, including sub-2 micron features at printing speeds of ~1 m/s. Finally, in conjunction with high-performance materials, we describe our realization of high-performance fully printed transistors on plastic, offering high-switching speed, excellent process throughput, and good fidelity over large areas.

Machine learning (ML) as a predictive methodology can potentially reduce the configuration cost and workload of inkjet printing. Inkjet printing has many advantages for additive manufacturing and printed electronics including low cost, scalability, non-contact printing and on-demand customization. Inkjet generates droplets with a piezoelectric dispenser controlled through frequency, voltage pulse and timing parameters. A major challenge is the design of jettable inks and the rapid optimization of stable jetting conditions whilst preventing common problems (no ejection, perturbation, satellite drop, multiple drops, drop breaking, nozzle clogging). Material consuming trial and error experiments are replaced here with a ML based jetting window. A dataset of machine and material properties is created from literature and experimental data. After exploratory data analysis and feature identification, various (linear and non-linear) regression models are compared in detail. The models are trained on 80% of the data and root mean square error (RMSE) is calculated on 20% test data. Simple polynomial relationships between the input and output features yield coarse prediction. Instead, small ensembles of decision trees (DTs) (boosted DTs and random forests) have improved predictive power for drop velocity and radius with RMSE of 0.39 m s⁻¹ and 2.21 µm respectively. The mean absolute percentage error is 3.87%. The models are validated with experimentally collected data for a novel ink where no data points with this ink were included in the training set. Additionally, several classification algorithms are utilized to categorize ink and printer parameters by jetting regime (‘single drop’, ‘multiple drops’, ‘no ejection’). Categorization and regression models are combined to improve overall model prediction. This article demonstrates that ML can be used to predict ink jetting behavior from 11 different ink and printing parameters. Different algorithms are analyzed and the optimal combination of algorithms is identified. It is shown that experimental and literature data can be combined and an initial dataset is created that other reserachers can build on in the future. ML enables efficient material and printing parameter selection speeding up the development of novel ink materials for printed electronics by eliminating jetting experiments that are money, time and material intensive.

Unmanned Aerial Vehicles (UAVs) are increasingly used to deliver items and gather information in remote areas. As a result, UAVs suffer from ice accumulation on their wings, which drastically affects their flight. However, the UAV industry is still relatively young and, thus, commercial solutions for de-icing on UAVs are limited. The use of carbon fiber composites is becoming increasingly ubiquitous for UAVs as well as many other industries such as military, construction, medical, automobile, sporting goods and aircraft systems. Carbon fiber is commercially available in various forms, including single yarns, braids and weaves, which are similar to traditional textiles. This paper presents a method to create a self-heating composite structure that can be integrated into UAV wings for de-icing by exploiting the thermal and electrical conductivity of carbon fiber. Extrusion printing is used to fabricate electrical contacts directly on the carbon fiber weave. Extrusion printing on textiles faces multiple challenges that are overcome in this paper. A method is presented to extrusion print conductive paste on textiles. Specifically, carbon fiber weaves. This manufacturing method is employed to fabricate carbon fiber-based heating devices, and they are characterized electrically and thermally.