An Optimal Process Parameters prediction model for Wire Bonding of Ultra-Thin CSP Package Based on Hybrid Methods of Artificial Intelligence

This research combined the Taguchi method and artificial intelligence methods, used them as the prediction tool in wire bond designing parameters for an Ultra-Thin CSP package, and then constructed a set of the optimal parameter analysis flow and steps. This paper employed desirability function to integrate two quality characteristics (loop height and wire pull strength) into a single quality indicator to construct a well-trained neural network prediction model with hybrid genetic algorithm, thereby achieving the objective of improving process yield and robustness design of micro HDD driver IC. The engineers could quickly obtain the optimal production process parameter with the demand of multi-quality characteristics, and enhance the assembly quality and yield of driver IC of micro HDD .