An adaptive genetic algorithm optimize neural networks approach for wafer dicing saw quality prediction-Reference-Cited by-同舟云学术

An adaptive genetic algorithm optimize neural networks approach for wafer dicing saw quality prediction

Published:2022-11-22 Issue:1-2 Volume:124 Page:209-218
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Shi Jun,Du Sihan^ORCID,Shi Yunpeng,Cao Weifeng,Zhou Lintao

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s00170-022-10472-0.pdf

Reference25 articles.

1. Chen FJ, Ye XQ, Yin SH, Ye QS, Huang S, Tan-g QC (2019) Automated vision positioning system for dicing semiconductor chips using improved template matching method. Int J Adv Manuf Techn-ol 100(9–12):2669–2678

2. Wu JZ, Chen GC, Chen FJ (2020) Positioning accuracy control of dual-axis dicing saw for machining semiconductor chip. Int J Adv Manuf Technol 109(7):2299–2310

3. Lee SM (2017) Adoption of hybrid dicing technique to minimize sawing-induced damage during semiconductor wafer separation. Mater Trans 58(4):530–534

4. Sim HS, Kim YS, Kim Y, Bae BM, Yoon JH (2021) Semiconductor device, semiconductor chip an-d method of dicing a semiconductor substrate. KR US11107773B2 20210831

5. Kayabasi E, Ozturk S, Celik E, Kurt H (2017) Determination of cutting parameters for silicon wafer with a diamond wire saw using an artificial neural network. Sol Energy 149:285–293

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