Aerosol Jet® Printing (AJP) is a direct-write based additive manufacturing process that is capable of printing electronics with fine features and various materials. It eliminates the complex masking process in traditional semiconductor manufacturing, thus enables flexible electronics design and reduces manufacturing cost. However, the quality control of AJP processes is still a challenging problem, primarily due to the lack of understanding of the potential root causes of the quality issues. There is a complex interaction among process setting variables, in situ feature variables, and quality variables in AJP processes. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the in situ feature variables, and the effect of the in situ feature variables on quality variables in a two-level hierarchical way. By identifying significant in situ feature variables as responses for the process setting variables, as well as predictors for product quality in a joint estimation problem, the proposed models have a hierarchical variable relationship to enable in situ process control for variation reduction and defect mitigation. A real case study is investigated to demonstrate the advantages of the proposed method. / Master of Science / Printed electronics is a promising technique for the future of the electronics manufacturing industry due to its potential for producing thin, flexible and low cost electronic devices. For the printing of any electronic device, a fundamental step is to print the conductive wires. Aerosol Jet® Printing (AJP) is one of the emerging additive manufacturing technologies for printing the conductive wires on a variety of substrates. It is a maskless additive manufacturing technique capable of printing high resolution wires. However, the quality control of AJP processes is still a challenging problem, primarily due to the lack of understanding of the potential root cause factors of the quality issues. There is a complex interaction among process setting variables, <i>in situ</i> feature variables, and quality variables. More importantly, the selection of the <i>in situ</i> feature variables is typically based on engineering domain knowledge and sensor instrumentation capability, rather than based on statistical significance of variables. In this research, an ensemble model strategy is proposed to quantify the effect of the process setting variables on the <i>in situ</i> feature variables, and the effect of the <i>in situ</i> feature variables on quality variables in a two-level hierarchical way. By identifying significant <i>in situ</i> feature variables as responses for the process setting variables, as well as predictors for product quality in a joint estimation problem, the proposed models have a hierarchical variable relationship to enable <i>in situ</i> process control for variation reduction and defect mitigation. A real case study is investigated to demonstrate the advantages of the proposed method.

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