gans for data augmentation

**GANs for Data Augmentation** in semiconductor manufacturing is the **use of Generative Adversarial Networks to generate realistic synthetic training data** — addressing the chronic shortage of labeled defect images, rare process conditions, and imbalanced datasets that limit ML model performance. **GAN Architectures for Fab Data** - **DCGAN**: Deep convolutional GAN for basic image generation. - **Conditional GAN (cGAN)**: Generates specific defect types conditioned on class labels. - **WGAN-GP**: Wasserstein GAN with gradient penalty for stable training. - **CycleGAN**: Translates between domains (e.g., optical ↔ SEM images) without paired data. **Why It Matters** - **Data Scarcity**: Real defect images are scarce and expensive to label — GANs multiply the dataset. - **Improved Accuracy**: GAN-augmented training typically improves classifier accuracy by 5-15%. - **Balanced Training**: Generate minority-class samples to balance severely imbalanced datasets. **GANs for Augmentation** are **the data multiplier** — using adversarial generation to create realistic synthetic fab data that improves ML model training and robustness.