support vector machines for classification

**SVM** (Support Vector Machine) for semiconductor classification is the **application of maximum-margin classifiers to separate process conditions or wafer types** — finding the hyperplane that maximally separates classes in feature space, with kernel functions handling non-linear boundaries. **How Does SVM Work?** - **Margin**: Find the hyperplane that maximizes the distance to the nearest data points (support vectors). - **Kernel Trick**: Map data to higher-dimensional space (RBF, polynomial kernels) for non-linear boundaries. - **Soft Margin**: Allow some misclassifications (controlled by parameter $C$) for noisy data. - **Multi-Class**: One-vs-one or one-vs-all strategies for multi-class problems. **Why It Matters** - **Small Datasets**: SVMs excel when training data is limited — common early in a new process development. - **Feature Space**: Kernel SVMs can model complex, non-linear decision boundaries efficiently. - **Defect Classification**: Effective for wafer map pattern classification and defect type identification. **SVM** is **the maximum-margin classifier** — finding the widest possible gap between classes for robust classification of semiconductor data.