Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment

He Yunbo; Hu Yongshan; Chen Xin; Gao Jian; Yang Zhijun; Chen Yun; Tang Hui; Ao Yinhui; Zhang Yu

doi:doi:10.11648/j.acis.20160402.15

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Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment

He Yunbo, Hu Yongshan, Chen Xin, Gao Jian, Yang Zhijun, Chen Yun, Tang Hui, Ao Yinhui, Zhang Yu

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 2)

Received: 17 April 2016 Published: 19 April 2016

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Abstract

It’s always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.

Published in	Automation, Control and Intelligent Systems (Volume 4, Issue 2)
DOI	10.11648/j.acis.20160402.15
Page(s)	35-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Dynamic Performance, Settling Performance, Electronic Packaging Equipment, Fuzzy Logic, Fine Tuning, Machine Intelligence

References

[1]	Iuliana R., Maarten S., Rogier E., "Adaptive Iterative Learning Control for High Precision Motion Systems," IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, VOL. 16, NO. 5, SEPTEMBER 2008, pp. 1075-1082.
[2]	Aurelio P., Antonio V., "An Iterative Approach for Noncausal Feedforward Tuning", Proceedings of the 2007 American Control Conference, Marriott Marquis Hotel at Times Square, New York City, USA, July 11-13, 2007, pp. 1251-1256.
[3]	Marcel H., Daan H., Maarten S., "MIMO feed-forward designin wafer scanners using a gradient approximation-based algorithm" Control EngineeringPractice18(2010), pp. 495–506.
[4]	Stearns H., Mishra S., Tomizuka M., “Iterative Tuning of Feedforward Controller with Force Ripple Compensation for Wafer Stage,” 10th IEEE International Workshop, AMC '08, Trento, Italy, 2008, pp. 234-239.
[5]	Morteza M., "Self-tuning PID controller to three-axis stabilization of a satellite with unknown parameters", International Journal of Non-Linear Mechanics 49 (2013), pp. 50–56.
[6]	Meric C., SerdarI., "A novel auto-tuning PID control mechanism for nonlinear systems", ISA Transactions 58 (2015), pp. 292–308.
[7]	Onur K., MujdeG., IbrahimE., EnginY., TufanK., "Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration", Engineering Applications of Artificial Intelligence 26 (2013) , pp. 184–197.
[8]	Osama E., Mohammad E., Nabila M., “Development of Self -Tuning Fuzzy Iterative Learning Control for Controlling a Mechatronic System,” International Journal of Information and Electronics Engineering, Vol. 2, No. 4(2012), pp. 565-569.
[9]	Timothy P. K., Allon G., Shubham K. B., “Model-based Optoelectronic Packaging Automation,” IEEE Journal of Selected Topics in Quantum Electronics, Vol. 10, No. 3 (2004), pp. 445-454.
[10]	Frank B., Tom O., Maarten S., “Accuracy Aspects in Motion Feedforward Tuning,” 2014 American Control Conference (ACC), Portland, Oregon, USA, 2014, pp. 2178-2183.
[11]	Dennis B., Niels V. D., “Combined Input Shaping and Feedforward Control for Flexible Motion Systems,” 2012 American Control Conference, Fairmont Queen Elizabeth, Montréal, Canada, 2012, pp. 2473-2478.
[12]	Yunbo H., Xin C., etc., Invited paper, 17th Electronics Packaging Technology Conference, EPTC 2015 Singapore, “Development of High Performance Bonding Machines with Improved Motion Control and Intelligent Fine Tuning Algorithm,” 2-4 December 2015.

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APA Style

He Yunbo, Hu Yongshan, Chen Xin, Gao Jian, Yang Zhijun, et al. (2016). Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Automation, Control and Intelligent Systems, 4(2), 35-41. https://doi.org/10.11648/j.acis.20160402.15

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ACS Style

He Yunbo; Hu Yongshan; Chen Xin; Gao Jian; Yang Zhijun, et al. Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Autom. Control Intell. Syst. 2016, 4(2), 35-41. doi: 10.11648/j.acis.20160402.15

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AMA Style

He Yunbo, Hu Yongshan, Chen Xin, Gao Jian, Yang Zhijun, et al. Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Autom Control Intell Syst. 2016;4(2):35-41. doi: 10.11648/j.acis.20160402.15

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@article{10.11648/j.acis.20160402.15,
  author = {He Yunbo and Hu Yongshan and Chen Xin and Gao Jian and Yang Zhijun and Chen Yun and Tang Hui and Ao Yinhui and Zhang Yu},
  title = {Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {35-41},
  doi = {10.11648/j.acis.20160402.15},
  url = {https://doi.org/10.11648/j.acis.20160402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.15},
  abstract = {It’s always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.},
 year = {2016}
}

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TY  - JOUR
T1  - Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment
AU  - He Yunbo
AU  - Hu Yongshan
AU  - Chen Xin
AU  - Gao Jian
AU  - Yang Zhijun
AU  - Chen Yun
AU  - Tang Hui
AU  - Ao Yinhui
AU  - Zhang Yu
Y1  - 2016/04/19
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.15
DO  - 10.11648/j.acis.20160402.15
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 35
EP  - 41
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.15
AB  - It’s always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.
VL  - 4
IS  - 2
ER  -

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Author Information

He Yunbo

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Hu Yongshan

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Chen Xin

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Gao Jian

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Yang Zhijun

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Chen Yun

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Tang Hui

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Ao Yinhui

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China
Zhang Yu

Guangdong Provincial Key Lab. of Computer Integrated Manufacturing Systems, Key Laboratory of Mechanical Equipment Manufacturing and Control Technology of Ministry of Education, School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, P.R. China

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Plain Text BibTeX RIS

APA Style

He Yunbo, Hu Yongshan, Chen Xin, Gao Jian, Yang Zhijun, et al. (2016). Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Automation, Control and Intelligent Systems, 4(2), 35-41. https://doi.org/10.11648/j.acis.20160402.15

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ACS Style

He Yunbo; Hu Yongshan; Chen Xin; Gao Jian; Yang Zhijun, et al. Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Autom. Control Intell. Syst. 2016, 4(2), 35-41. doi: 10.11648/j.acis.20160402.15

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AMA Style

He Yunbo, Hu Yongshan, Chen Xin, Gao Jian, Yang Zhijun, et al. Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment. Autom Control Intell Syst. 2016;4(2):35-41. doi: 10.11648/j.acis.20160402.15

Copy | Download

@article{10.11648/j.acis.20160402.15,
  author = {He Yunbo and Hu Yongshan and Chen Xin and Gao Jian and Yang Zhijun and Chen Yun and Tang Hui and Ao Yinhui and Zhang Yu},
  title = {Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {2},
  pages = {35-41},
  doi = {10.11648/j.acis.20160402.15},
  url = {https://doi.org/10.11648/j.acis.20160402.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160402.15},
  abstract = {It’s always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.},
 year = {2016}
}

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TY  - JOUR
T1  - Fuzzy Fine Tuning Therapies for Intelligence of High Speed Electronic Packaging Equipment
AU  - He Yunbo
AU  - Hu Yongshan
AU  - Chen Xin
AU  - Gao Jian
AU  - Yang Zhijun
AU  - Chen Yun
AU  - Tang Hui
AU  - Ao Yinhui
AU  - Zhang Yu
Y1  - 2016/04/19
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160402.15
DO  - 10.11648/j.acis.20160402.15
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 35
EP  - 41
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160402.15
AB  - It’s always difficult to satisfy both strict requirements of dynamic performance and settling performance of high-precision and high-acceleration point-to-point motions on electronic packaging equipment, i.e., Die bonders, Wire bonders, Flip-chip bonders, Wafer bumping machines, and so on. Focusing on this difficulty, Fuzzy logic is promoted in this paper to fine tune feed forward coefficient parameters of motion systems on packaging equipment to satisfy multiple targets of dynamic and settling performance requirements. This intelligent fine tuning approach, with achievable multiple motion targets, provides a therapy to automate and optimize motion system performance on packaging equipment, help on development and mass production of semiconductor packaging equipment, and improve machine intelligence.
VL  - 4
IS  - 2
ER  -

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