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.