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Research Papers

Quality Modeling of Printed Electronics in Aerosol Jet Printing Based on Microscopic Images

[+] Author and Article Information
Hongyue Sun

Mem. ASME
Grado Department of Industrial
and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24061
e-mail: hongyue@vt.edu

Kan Wang

Mem. ASME
H. Milton Stewart School of Industrial
and Systems Engineering,
Georgia Tech,
Atlanta, GA 30332
e-mail: kwang34@mail.gatech.edu

Yifu Li

Grado Department of Industrial
and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24061
e-mail: liyifu@vt.edu

Chuck Zhang

H. Milton Stewart School of Industrial
and Systems Engineering,
Georgia Tech,
Atlanta, GA 30332
e-mail: chuck.zhang@gatech.edu

Ran Jin

Mem. ASME
Grado Department of Industrial
and Systems Engineering,
Virginia Tech,
Blacksburg, VA 24061
e-mail: jran5@vt.edu

1Corresponding author.

Manuscript received March 30, 2016; final manuscript received December 19, 2016; published online April 10, 2017. Assoc. Editor: Sam Anand.

J. Manuf. Sci. Eng 139(7), 071012 (Apr 10, 2017) (10 pages) Paper No: MANU-16-1190; doi: 10.1115/1.4035586 History: Received March 30, 2016; Revised December 19, 2016

Aerosol jet printing (AJP) is a direct write technology that enables fabrication of flexible, fine scale printed electronics on conformal substrates. AJP does not require the time consuming mask and postpatterning processes compared with traditional electronics manufacturing techniques. Thus, the cycle time can be dramatically reduced, and highly personalized designs of electronics can be realized. AJP has been successfully applied to a variety of industries, with different combinations of inks and substrates. However, the quality of the printed electronics, such as resistance, is not able to be measured online. On the other hand, the microscopic image sensors are widely used for printed circuit boards (PCBs) quality quantification and inspection. In this paper, two widely used quality variables of printed electronics, resistance and overspray, will be jointly modeled based on microscopic images for fast quality assessment. Augmented quantitative and qualitative (AUGQQ) models are proposed to use features of microscopic images taken at different locations on the printed electronics as input variables, and resistance and overspray as output variables. The association of resistance and overspray can be investigated through the AUGQQ models formulation. A case study for fabricating silver lines with Optomec® aerosol jet system is used to evaluate the model performance. The proposed AUGQQ models can help assess the printed electronics quality and identify important image features in a timely manner.

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Figures

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Fig. 1

An illustration of the image and quality measurements of the jth line and the basic resistance model formulation

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Fig. 2

Proposed augmented QQ modeling framework

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Fig. 3

Optomec® aerosol jet system and printed silver nanoparticle lines. The 6th and 7th images from two lines with resistance 22.2 Ω and 32.8 Ω are provided in 1, 2 and 3, 4 separately.

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Fig. 4

Examples of image features extracted from Image 2 in Fig. 3: (a) three largest dark areas that are used for features 25–27, (b) effective area that is used for feature 28, (c) intensity distribution that is used for features 63–82, where the horizontal axis is the image intensity, and the vertical axis is the count, and (d) largest maximally stable extremal region that is used for features 84–96

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Fig. 5

Selected features: (a) selected PCs of image features and (b) selected original image features

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Fig. 6

Presentative resistance versus feature plots: (a) resistance versus PC 1, (b) resistance versus PC 2, (c) resistance versus histogram (20th bin), and (d) resistance versus MSER area

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Fig. 7

Presentative features for overspray (star: overspray; circle: normal): (a) PC 1: normal versus overspray, (b) PC 3: normal versus overspray, (c) effective area: normal versus overspray, and (d) relative max. pixel: normal versus overspray

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Fig. 8

Residual plots for resistance: (a) QQ plot for residual, (b) residual versus predicted, (c) residual versus samples, and (d) autocorrelation of residuals

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Fig. 9

Logistic regression prediction result

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