Cell filling in gravure printing for printed electronics

Advanced Algorithm for Reliable Quantification of the Geometry and Printability of Printed Patterns

In nanoparticle-based printed electronic devices, the printability of the patterns constituting the device are crucial factors. Although many studies have investigated the printability of patterns, only a few have analyzed and established international standards for measuring the dimensions and printability of shape patterns. This study introduces an advanced algorithm for accurate measurement of the geometry and printability of shape patterns to establish an international standard for pattern dimensions and printability. The algorithm involves three core concepts: extraction of edges of printed patterns and identification of pixel positions, identification of reference edges via the best-fitting of the shape pattern, and calculation of different pixel positions of edges related to reference edges. This method enables the measurement of the pattern geometry and printability, including edge waviness and widening, while considering all pixels comprising the edges of the patterns. The study results revealed that the rectangle and circle patterns exhibited an average widening of 3.55% and a maximum deviation of 1.58%, based on an average of 1662 data points. This indicates that the algorithm has potential applications in real-time pattern quality evaluation, process optimization using statistical or AI-based methods, and foundation of International Electrotechnical Commission standards for shape patterns.

Machine Learning Enables Process Optimization of Aerosol Jet 3D Printing Based on the Droplet Morphology

Aerosol jet printing (AJP) is a promising noncontact direct ink writing technology that enables flexible and conformal electronic devices to be fabricated onto planar and nonplanar substrates with higher resolution and less waste. Despite possessing many advantages, the limited electrical performance of microelectronic devices caused by the poor printing quality is still the greatest hurdle to overcome for AJP technology. With the motivation to improve the printing quality, a novel hybrid machine learning method is proposed to analyze and optimize the AJP process based on the deposited droplet morphology in this study. The proposed method consists of classic machine learning approaches, including space-filling-based experimental design, clustering, classification, regression, and multiobjective optimization. In the proposed method, a two-dimensional (2D) design space is fully explored using a Latin hypercube sampling approach for experimental design, and a K-means clustering approach is employed to reveal the cause-effect relationship between the deposited droplet morphology and printed line characteristics. Following that, an optimal operating window with respect to the deposited droplet morphology is identified using a support vector machine to ensure the printing quality in a design space. Finally, to achieve high-controllability and sufficient-thickness droplets, Gaussian process regression is adopted to develop the process model of droplet geometrical properties, and the deposited droplet morphology is optimized under dual conflicting objectives of customizing the droplet diameter and maximizing droplet thickness. Different from previous printing quality optimization approaches, the proposed method enables a systemic investigation on the formation mechanisms of printed line characteristics, and the printing quality is fundamentally optimized based on the deposited droplet morphology. Moreover, data-driven-based characteristics can help the proposed approach serve as a guideline for printing quality optimization in other noncontact direct ink writing technologies.

Mapping the Volume Transfer of Graphene-Based Inks with the Gravure Printing Process: Influence of Rheology and Printing Parameters

It is a common practice to add rheology modifiers to functional inks, such as graphene inks, to optimize the rheological properties so that they can be printed with a certain printing technique. This practice may lead to inks formulations with poorer electrical, optical, and mechanical performance upon its application, which are of paramount importance in printed electronics. In this study, we demonstrate for three different commercial graphene-based inks that it is possible to control the amount of ink transferred to the flat surface by tweaking printing parameters, such as the velocity and the length scale of the gravure cell, without modifying the rheology of the ink. Finally, the results are summarized in printing maps based on dimensionless numbers, namely, the capillary and Reynolds numbers.

Reviews on Machine Learning Approaches for Process Optimization in Noncontact Direct Ink Writing

Recently, machine learning has gained considerable attention in noncontact direct ink writing because of its novel process modeling and optimization techniques. Unlike conventional fabrication approaches, noncontact direct ink writing is an emerging 3D printing technology for directly fabricating low-cost and customized device applications. Despite possessing many advantages, the achieved electrical performance of produced microelectronics is still limited by the printing quality of the noncontact ink writing process. Therefore, there has been increasing interest in the machine learning for process optimization in the noncontact direct ink writing. Compared with traditional approaches, despite machine learning-based strategies having great potential for efficient process optimization, they are still limited to optimize a specific aspect of the printing process in the noncontact direct ink writing. Therefore, a systematic process optimization approach that integrates the advantages of state-of-the-art machine learning techniques is in demand to fully optimize the overall printing quality. In this paper, we systematically discuss the printing principles, key influencing factors, and main limitations of the noncontact direct ink writing technologies based on inkjet printing (IJP) and aerosol jet printing (AJP). The requirements for process optimization of the noncontact direct ink writing are classified into four main aspects. Then, traditional methods and the state-of-the-art machine learning-based strategies adopted in IJP and AJP for process optimization are reviewed and compared with pros and cons. Finally, to further develop a systematic machine learning approach for the process optimization, we highlight the major limitations, challenges, and future directions of the current machine learning applications.

Spontaneous rise in open rectangular channels under gravity

Fluid movement in microfluidic devices, porous media, and textured surfaces involves coupled flows over the faces and corners of the media. Spontaneous wetting of simple grooved surfaces provides a model system to probe these flows. This numerical study investigates the spontaneous rise of a liquid in an array of open rectangular channels under gravity, using the Volume-of-Fluid method with adaptive mesh refinement. The rise is characterized by the meniscus height at the channel center, outer face and the interior and exterior corners. At lower contact angles and higher channel aspect ratios, the statics and dynamics of the rise in the channel center show little deviation with the classical model for capillarity, which ignores the existence of corners. For contact angles smaller than 45°, rivulets are formed in the interior corners and a cusp at the exterior corner. The rivulets at long times obey the one-third power law in time, with a weak dependence on the geometry. The cusp behaviour at the exterior corner transforms into a smooth meniscus when the capillary force is higher in the channel, even for contact angles smaller than 45°. The width of the outer face does not influence the capillary rise inside the channel, and the channel size does not influence the rise on the outer face.

Printable Nanomaterials for the Fabrication of High-Performance Supercapacitors

In recent years, supercapacitors are attracting great attention as one kind of electrochemical energy storage device, which have a high power density, a high energy density, fast charging and discharging, and a long cycle life. As a solution processing method, printing technology is widely used to fabricate supercapacitors. Printable nanomaterials are critical to the fabrication of high-performance supercapacitors by printing technology. In this work, the advantages of printing technology are summarized. Moreover, various nanomaterials used to fabricate supercapacitors by printing technology are presented. Finally, the remaining challenges and broad research as well as application prospects in printing high-performance supercapacitors with nanomaterials are proposed.

Recent Progress in the Development of Printed Thin-Film Transistors and Circuits with High-Resolution Printing Technology

Printed electronics enable the fabrication of large-scale, low-cost electronic devices and systems, and thus offer significant possibilities in terms of developing new electronics/optics applications in various fields. Almost all electronic applications require information processing using logic circuits. Hence, realizing the high-speed operation of logic circuits is also important for printed devices. This report summarizes recent progress in the development of printed thin-film transistors (TFTs) and integrated circuits in terms of materials, printing technologies, and applications. The first part of this report gives an overview of the development of functional inks such as semiconductors, electrodes, and dielectrics. The second part discusses high-resolution printing technologies and strategies to enable high-resolution patterning. The main focus of this report is on obtaining printed electrodes with high-resolution patterning and the electrical performance of printed TFTs using such printed electrodes. In the final part, some applications of printed electronics are introduced to exemplify their potential.