An empirical approach to modelling fluid dispensing for electronic packaging

Fluid dispensing is a popular process in semiconductor manufacturing industry which is commonly used in die-bonding as well as microchip encapsulation for electronic packaging. Modelling the fluid dispensing process is important to understand the process behaviour as well as determine optimum operating conditions of the process for a high-yield, low cost and robust operation. Previous studies of fluid dispensing mainly focus on the development of analytical models. However, an analytical model for fluid dispensing, which can provide accurate results, is very difficult to develop because of the complex behaviour of fluid dispensing and high degree of uncertainties of the process in a real world environment. In this project, an empirical approach to modelling fluid dispensing was attempted. Two common empirical modelling techniques, statistical regression and neural networks, were introduced to model fluid dispensing process for electronic packaging. Development of neural network based process models using genetic algorithm (GA) and Levenberg−Marquardt algorithm are presented. Validation tests were performed to evaluate the effectiveness of the developed process models from which a multiple regression model and a GA trained neural network with the architecture of 3-15-1 were identified to be the process models of the fluid dispensing respectively for the encapsulation weight and encapsulation thickness.