Research Papers

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

[+] Author and Article Information
Hongyue Sun

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

Kan Wang

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

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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Hoey, J. M. , Lutfurakhmanov, A. , Schulz, D. L. , and Akhatov, I. S. , 2012, “ A Review on Aerosol-Based Direct-Write and Its Applications for Microelectronics,” J. Nanotechnol., 57(2), pp. 1–22. [CrossRef]
Hon, K. K. B. , Li, L. , and Hutchings, I. M. , 2008, “ Direct Writing Technology—Advances and Developments,” CIRP Ann.-Manuf. Technol., 57(2), pp. 601–620. [CrossRef]
Zhao, D. , Liu, T. , Park, J. G. , Zhang, M. , Chen, J. M. , and Wang, B. , 2012, “ Conductivity Enhancement of Aerosol-Jet Printed Electronics by Using Silver Nanoparticles Ink With Carbon Nanotubes,” Microelectron. Eng., 96, pp. 71–75. [CrossRef]
Mahajan, A. , Frisbie, C. D. , and Francis, L. F. , 2013, “ Optimization of Aerosol Jet Printing for High-Resolution, High-Aspect Ratio Silver Lines,” ACS Appl. Mater. Interfaces, 5(11), pp. 4856–4864. [CrossRef] [PubMed]
Verheecke, W. , Van Dyck, M. , Vogeler, F. , Voet, A. , and Valkenaers, H. , 2012, “ Optimizing Aerosol Jet Printing of Silver Interconnects on Polyimide Film for Embedded Electronics Applications,” 8th International Conference of DAAAM Baltic Industrial Engineering, T. Otto ed., Tallinn, Estonia, pp. 373–379.
Ha, M. , Seo, J. W. T. , Prabhumirashi, P. L. , Zhang, W. , Geier, M. L. , Renn, M. J. , Kim, C. H. , Hersam, M. C. , and Frisbie, C. D. , 2013, “ Aerosol Jet Printed, Low Voltage, Electrolyte Gated Carbon Nanotube Ring Oscillators With Sub-5 μs Stage Delays,” Nano Lett., 13(3), pp. 954–960. [CrossRef] [PubMed]
Perez, K. B. , and Williams, C. B. , 2013, “ Combining Additive Manufacturing and Direct Write for Integrated Electronics—A Review,” 24th International Solid Freeform Fabrication Symposium, D. L. Bourell ed., Austin, TX, pp. 962–979.
Goth, C. , Putzo, S. , and Franke, J. , 2011, “ Aerosol Jet Printing on Rapid Prototyping Materials for Fine Pitch Electronic Applications,” IEEE 61st Electronic Components and Technology Conference, Lake Buena Vista, FL, pp. 1211–1216.
Navratil, J. , Hamacek, A. , Reboun, J. , and Soukup, R. , 2015, “ Perspective Methods of Creating Conductive Paths by Aerosol Jet Printing Technology,” 38th IEEE International Spring Seminar on Electronics Technology, Eger, Hungary, pp. 36–39.
Seifert, T. , Sowade, E. , Roscher, F. , Wiemer, M. , Gessner, T. , and Baumann, R. R. , 2015, “ Additive Manufacturing Technologies Compared: Morphology of Deposits of Silver Ink Using Inkjet and Aerosol Jet Printing,” Ind. Eng. Chem. Res., 54(2), pp. 769–779. [CrossRef]
Sukeshini, A. , Jenkins, T. , Gardner, P. , Miller, R. , and Reitz, T. , 2010, “ Investigation of Aerosol Jet Deposition Parameters for Printing SOFC Layers,” ASME Paper No. Paper No. FuelCell2010-33262.
Shojib Hossain, M. , Espalin, D. , Ramos, J. , Perez, M. , and Wicker, R. , 2014, “ Improved Mechanical Properties of Fused Deposition Modeling-Manufactured Parts Through Build Parameter Modifications,” ASME J. Manuf. Sci. Eng., 136(6), p. 61002. [CrossRef]
Tapia, G. , and Elwany, A. , 2014, “ A Review on Process Monitoring and Control in Metal-Based Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 136(6), p. 060801. [CrossRef]
Rao, P. K. , Liu, J. P. , Roberson, D. , Kong, Z. J. , and Williams, C. , 2015, “ Online Real-Time Quality Monitoring in Additive Manufacturing Processes Using Heterogeneous Sensors,” ASME J. Manuf. Sci. Eng., 137(6), p. 061007. [CrossRef]
Zhang, P. , Toman, J. , Yu, Y. , Biyikli, E. , Kirca, M. , Chmielus, M. , and To, A. C. , 2015, “ Efficient Design-Optimization of Variable-Density Hexagonal Cellular Structure by Additive Manufacturing: Theory and Validation,” ASME J. Manuf. Sci. Eng., 137(2), p. 021004. [CrossRef]
Huang, Y. , Leu, M. C. , Mazumder, J. , and Donmez, A. , 2015, “ Additive Manufacturing: Current State, Future Potential, Gaps and Needs, and Recommendations,” ASME J. Manuf. Sci. Eng., 137(1), p. 014001. [CrossRef]
Rao, P. K. , Kong, Z. , Duty, C. E. , Smith, R. J. , Kunc, V. , and Love, L. J. , 2015, “ Assessment of Dimensional Integrity and Spatial Defect Localization in Additive Manufacturing Using Spectral Graph Theory,” ASME J. Manuf. Sci. Eng., 138(5), p. 051007. [CrossRef]
Huang, Q. , 2016, “ An Analytical Foundation for Optimal Compensation of Three-Dimensional Shape Deformation in Additive Manufacturing,” ASME J. Manuf. Sci. Eng., 138(6), p. 061010. [CrossRef]
Mar, N. S. S. , Yarlagadda, P. K. D. V. , and Fookes, C. , 2011, “ Design and Development of Automatic Visual Inspection System for PCB Manufacturing,” Rob. Comput. Integr. Manuf., 27(5), pp. 949–962. [CrossRef]
Wang, K. , Chang, Y. H. , Zhang, C. , and Wang, B. , 2013, “ Evaluation of Quality of Printed Strain Sensors for Composite Structural Health Monitoring Applications,” SAMPE Fall Technical Conference, Wichita, KS, Oct. 21–24.
Meltzer, J. , Yang, M. H. , Gupta, R. , and Soatto, S. , 2004, “ Multiple View Feature Descriptors From Image Sequences Via Kernel Principal Component Analysis,” Computer Vision-ECCV 2004, Springer, Berlin, pp. 215–227.
Deng, X. , and Jin, R. , 2015, “ QQ Models: Joint Modeling for Quantitative and Qualitative Quality Responses in Manufacturing Systems,” Technometrics, 57(3), pp. 320–331. [CrossRef]
Yuan, M. , and Lin, Y. , 2006, “ Model Selection and Estimation in Regression With Grouped Variables,” J. R. Stat. Soc. Ser. B Stat. Methodol., 68(1), pp. 49–67. [CrossRef]
Breiman, L. , 1995, “ Better Subset Regression Using the Non-negative Garrote,” Technometrics, 37(4), pp. 373–384. [CrossRef]
Hastie, T. , Tibshirani, R. , and Friedman, J. H. , 2009, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York.
Optomec Inc., 2014, “ Aerosol Jet Printed Electronics Overview,” Optomec, Albuquerque, NM.
Tuytelaars, T. , and Mikolajczyk, K. , 2008, “ Local Invariant Feature Detectors: A Survey,” Foundations and Trends in Computer Graphics and Vision, 3(3), pp. 177–280.
Ke, Y. , and Sukthankar, R. , 2004, “ PCA-SIFT: A More Distinctive Representation for Local Image Descriptors,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, Vol. 2, pp. 506–513.
Jia, X. , and Richards, J. A. , 1999, “ Segmented Principal Components Transformation for Efficient Hyperspectral Remote-Sensing Image Display and Classification,” IEEE Trans. Geosci. Remote Sens., 37(1), pp. 538–542.
Sun, H. , Deng, X. , Wang, K. , and Jin, R. , 2016, “ Logistic Regression for Crystal Growth Process Modeling Through Hierarchical Nonnegative Garrote Based Variable Selection,” IIE Trans., 48(8), pp. 787–796. [CrossRef]
Abdelsamie, M. , Zhao, K. , Niazi, M. R. , Chou, K. W. , and Amassian, A. , 2014, “ In Situ UV-Visible Absorption During Spin-Coating of Organic Semiconductors: A New Probe for Organic Electronics and Photovoltaics,” J. Mater. Chem. C, 2(17), pp. 3373–3381. [CrossRef]


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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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