A Semiconductor Manufacturing Final Test Yield Classification Using Random Forest

Semi-conductor manufacturing companies are growing rapidly by forming collaborative design and wafer fabrication. The yield prediction techniques use production data and provide improved operational efficiency and decrease production costs. Random forest classification method is proposed for semiconductor manufacturing Final Test (FT) yield prediction. Data pre-processing methods include Gaussian mixture Model, one hot encoder and Label encoder whereas improved grid search algorithm is used for feature selection. The selected features are then given to the implemented classification Random forest (RF) model. The RF method was evaluated by conducting experiments for three different products which are provided by Silicon Laboratories. The RF method outperformed in handling both numerical and categorical production data and is evaluated by the metrics such as accuracy, precision, recall and F-measure. The developed method achieved 0.985 of accuracy when compared with the existing methods such as LSTM-AM and SH-DNN.