A hybrid intelligent approach for optimizing the fine-pitch copper wire bonding process with multiple quality characteristics in IC assembly

摘要

Gold is the primary material used for wire bonding in integrated circuit (IC) assembly. Owing to the high appreciation in the price of gold, copper (Cu) wire has become an important substitute material in order to save on manufacturing costs. However, an average of 40% in yield loss during IC assembly can be attributed to improper control of the Cu wire bonding process. To assure cost savings without losing yield, and ensure cost-effective IC assembly, optimization of the parameters for the Cu wire process is critical. This work proposes a hybrid intelligent approach to derive robust parameter settings for a fine-pitch Cu wire bonding process with multiple quality characteristics. The proposed methodology utilizes grey relational analysis and an entropy measurement method to convert the multiple responses into a single synthetic performance index without involving the subjective judgment of an engineer and causing unbalanced improvements of the responses. An integrated neural network model and genetic algorithm method is then applied to acquire the optimal parameter settings. The performance of this method is evaluated experimentally and the results compared with that of the response surface methodology and original parameter settings. The results confirm the feasibility and practicality of this strategy to improve production yield and process capability during Cu wire bonding.