Enhancing the Accuracy of Manufacturing Process Error Detection Through SMOTE-Based Oversampling Using Machine Learning and Deep Learning Techniques

A production competency study leads to a rise in the manufacturing sectors' strategic emphasis. Developing semiconductor materials is a highly complex approach that necessitates numerous evaluations. It is impossible to emphasize the significance of the quality of the product. We put up a number of methods for automatically creating a prognostic model that is effective at identifying equipment flaws throughout the semiconductor materials' wafer fabrication process. The SECOM dataset is representative of semiconductor production procedures that go through numerous tests performed. There are imbalanced statistics in the dataset, so our proposed methodology incorporates SMOTE (Synthetic Minority Over-sampling Technique) functionality that is introduced to mitigate the imbalance of the training dataset by leveling off any unbalanced attributes. Detecting faults in the manufacturing process improves semiconductor quality and testing efficiency, and is used to validate both approaches to Machine Learning and Deep Learning algorithms. This is accomplished by collecting performance metrics during the development process. Another aspect of our effort to cut down on the training time for testing is highlighted in our research report.