An artificial neural network approach for wafer dicing saw quality prediction. (December 2018)

• Record Type: Journal Article
• Authors: Su, Te-Jen; Chen, Yi-Feng; Cheng, Jui-Chuan; Chiu, Chien-Liang
• Is Part Of: Microelectronics and reliability. Volume 91(2018)Part 2
• Page Start: 257
• Page End: 261
• Publication Date: 2018-12
• Subjects: Wafer dicing saw -- Back propagation neural network -- Quality prediction; Electronic apparatus and appliances -- Reliability -- Periodicals; Miniature electronic equipment -- Periodicals; Appareils électroniques -- Fiabilité -- Périodiques; Équipement électronique miniaturisé -- Périodiques; Electronic apparatus and appliances -- Reliability; Miniature electronic equipment; Periodicals; 621.3815
• DOI: 10.1016/j.microrel.2018.10.013
• Languages: English
• ISSNs: 0026-2714

Abstract

Abstract: The wafer dicing saw is a critical process of the semiconductor IC assembly manufacturing, where chipping and cracking can damage internal circuits and result in device failure. This article applies back propagation neural networks to learn and analyze data transmitted from the machine sensors for predictive purposes. Experimental results show that neural network processing provides prediction accuracy of 75%. The results can be monitored during real-time machine operation. If predictive values exceed a certain specification, an alarm can be triggered to prevent product loss, thus increasing production efficiency.