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Using a neural-fuzzy approach for improving the perpendicular magnetic recording head manufacturing process

Magnetic write width targeting is a critical process in achieving high process yield and good quality in manufacturing perpendicular magnetic recording heads for hard disk drives. The current approach is an experimentally based feedback process in which the quality is highly dependent on the testing accuracy and the sensitivity assumptions. This paper proposes a new approach based on neural-fuzzy based modelling for improving the quality of the magnetic write width targeting. The model consists of a neural network for: 1) suggesting changes in the lapping parameters, together with a fuzzy reasoning mechanism; 2) obtaining fine-tuned lapping parameter values based on the parameters derived from the neural network. Through improvement in the magnetic write width targeting process, the overall process yield, and product quality can be improved and the overall cost of the magnetic recording heads will be significantly reduced.