Abstract Aerosol jet printing (AJP) is a promising non-contact writing technology to fabricate customized and conformal microelectronics devices on flexible substrates. However, in recent years, the printed line quality is highlighted as a limitation in the applications of AJP technology. According to previous researches, a line printed with high edge roughness and low cross-sectional area will reduce the resistance homogeneity and current carrying capacity, respectively. Despite a high line thickness is beneficial to increase the cross-sectional area, it will be in contradiction with a customized line width under a certain mass flow rate, and may lead to an increase in the line edge roughness. Therefore, it is necessary to minimize the inherent contradictions between different printed line features in a design space. In this research, a multi-objective optimization framework is proposed to optimize the overall printing quality of customized line width. In the proposed framework, Latin hyper sampling is utilized for initial experimental design as it could maximize uniformity in a design space with small dataset. Gaussian process regression (GPR) is then adopted for rapid modeling of the printed line morphology due to its capability of providing prediction uncertainty. Following that, GPR models are driven with an efficient multi-objective genetic algorithm to minimize the inherent contradictions of the AJP process. Thus, the optimal process parameters for customized line width printing can be identified systematically and cost-efficiently in a design space. Experimental results indicate the validity of the proposed framework for customized line width printing. Till now, there are few systematic researches on the optimization of printed line morphology, which is an essential component for AJP. This research attempts to contribute to enriching the body of knowledge on printing process optimization.

中文翻译：

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基于小数据集和预测不确定性的气溶胶喷射定制线宽打印多目标优化框架

摘要 气溶胶喷射印刷 (AJP) 是一种很有前途的非接触式书写技术，可用于在柔性基板上制造定制的保形微电子器件。然而，近年来，印制线路质量成为AJP技术应用中的一个限制因素。根据以往的研究，边缘粗糙度高、截面积低的印刷线会分别降低电阻均匀性和载流能力。尽管高线宽有利于增加截面积，但在一定质量流量下与定制线宽相矛盾，可能导致线边缘粗糙度增加。因此，有必要尽量减少设计空间中不同印刷线特征之间的内在矛盾。在这项研究中，提出了一个多目标优化框架来优化定制线宽的整体印刷质量。在提议的框架中，拉丁超采样用于初始实验设计，因为它可以在具有小数据集的设计空间中最大化均匀性。由于其提供预测不确定性的能力，因此采用高斯过程回归 (GPR) 对印刷线形态进行快速建模。随后，探地雷达模型由高效的多目标遗传算法驱动，以最大限度地减少 AJP 过程的内在矛盾。因此，可以在设计空间中系统地、经济地确定定制线宽印刷的最佳工艺参数。实验结果表明所提出的用于定制线宽印刷的框架的有效性。直到现在，对印刷线形态优化的系统研究很少，这是AJP必不可少的组成部分。该研究试图有助于丰富有关印刷工艺优化的知识体系。