Using improved simulated anneal arithmetic to improve semiconductor packaging ball placement process capability

Abstract

This study used improved simulated annealing arithmetic to improve the semiconductor packaging ball placement process capability. The simulated annealing computing process was combined with an artificial neural network, Metropolis algorithm, and sequential Gaussian simulation. Grey relation analysis was used as the target value, which is intended to obtain the optimum parameter design of process capability. The results showed that the substrate design-Pad Open is 0.30 mm ball size, 0.275 mm Pad open, Profile type B, maximum temperature 236 (°C), solder melting time 53 (s), preheating temperature 176 (°C), and a solder ball component, as SAC405 can result in a parameter design for optimal solder ball shear strength of C pk = 2.38 and G 0i = 0:87. This optimum parameter design process is better than other methods, and it reduces the cost waste of extra defectives.

Intelligent manufacturing/production systems: Modeling, algorithms, and optimization

“…The research provides a novel method for reducing this through better approximation of the process itself and thus better ability to control the process. Wang and Li 10 propose a combined simulated annealing and artificial neural network approach to improve on placement in semi-conductor manufacturing. The method is demonstrated to have an improved performance over current state.…”