AI-Driven Semiconductor Manufacturing via IC Failure Prediction: Cost-Effective Strategies for Process Improvement

Integrated circuits (ICs) are fundamental components in a wide range of applications, including consumer electronics, automotive systems, telecommunications, and medical devices, where their reliable performance is crucial. However, identifying failures in ICs presents several challenges due to the complex nature of the circuits, variability in manufacturing processes, and the subtleties of faults that may not be easily detectable with traditional methods. To address these issues, a robust methodology incorporating data preprocessing, Synthetic Minority Over-sampling Technique (SMOTE) for data balancing, Principal Component Analysis (PCA) for feature extraction, and Random Forest classification is proposed. Data preprocessing involves cleaning and normalizing the dataset to ensure quality and consistency. SMOTE is then applied to address the imbalance in the dataset, enhancing the detection of minority class failures. PCA is used to reduce the dimensionality of the dataset, retaining essential features that capture the most variance, which improves the efficiency and accuracy of the model. Finally, Random Forest classification is employed due to its robustness and ability to handle large datasets with high dimensionality, providing a reliable prediction of IC failures.