A novel analytical filling time chart for design optimization of flip-chip underfill encapsulation process

• Published in: International journal of advanced manufacturing technology 2019-12, Vol.105 (7-8), p.3521-3530
• Main Authors: Ng, Fei Chong, Ali, M. Yusuf Tura, Abas, Aizat, Khor, C. Y., Samsudin, Z., Abdullah, M. Z.
• Format: Article
• Language: English
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Contact angle; Design optimization; Encapsulation; Engineering; Industrial and Production Engineering; Mechanical Engineering; Media Management; Original Article; Production costs; Regional analysis; Trends
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-019-04573-6

Underfill encapsulation process regularly encounters productivity issue of long filling time that incurs additional manufacturing costs. The package was optimized to attain least filling time while retaining the miniature package size. This paper presents a new analytical generalized filling time chart that was generated using the latest regional segregation-based analytical filling time model. The governing model was well-validated to the industrial underfill benchmark data with discrepancy of less than 10.42%. The filling time chart gives non-dimensionalized filling times at various combinations of bump pitch, gap height, and contact angle. Subsequently, another chart that gives the filling time coefficient was derived from the filling time chart to compute the instantaneous filling time directly. Interestingly, two variation trends of bump pitch were observed from the filling time chart that were distinguishable based on the critical contact angle. When the contact angle exceeds its critical value, there exists the critical bump pitch that restricted the miniature design of the package. On contrary, all past literatures only elucidated one variation trend of bump pitch without introduced the critical contact angle. Overall, the analytical filling time chart benefited the optimization study as it concisely composed all parametric variation trends of filling time as well as the criticality of underfill parameters. Thus, time and effort can be greatly reduced upon compared with the conventional numerical-based optimization work, without compromising the accuracy aspect.