Lot Quality Prediction in Semiconductor Manufacturing Final Test Using Multilayer Perceptron

Semiconductor manufacturing relies on final test (FT) to ensure the quality of integrated circuits before shipment. To optimize manufacturing and reduce costs, this study develops an AI model that can shorten the FT cycle time. Benefits include early detection of problematic lots, more accurate forecasting, and more thorough root cause analysis. Using a Multilayer Perceptron (MLP) to predict lot quality, all programming, training, and evaluation were coded using Python through Jupyter Notebook. Kruskal-Wallis and Dunn's tests showed that increasing hidden layers from two to five did not significantly improve accuracy. Afterward, hyperparameter tuning identified the best model configuration, resulting in to-fold cross-validation with an average classification accuracy of 88.4622%, an F1-score of 88.4309%, and an ROC-AUC of 96.2478%.