Neural network modeling of fabrication yield using manufacturing data

This paper describes the creation of artificial neural network models using production line data and illustrates their usefulness for process control in semiconductor manufacturing. Three artificial neural network models are created. The first models a high aspect ratio etch process. The other two are created to predict yield metrics from inline critical dimension (CD) measurements. One model predicts the number of faults on a die, and the other predicts the probability of die failure at probe. The high aspect ratio etch model has an average prediction error of 3.9%. The average prediction error for the number of faults on a die is 14.9%, and the average prediction error for probability of die failure at probe is 21.8%. A sensitivity analysis is performed on each model to illustrate how they can be used to judge the relative impact of each input.