Generative Adversarial Networks for Historical Data Generation in Semiconductor Manufacturing: Applications and Challenges

Semiconductor manufacturing relies heavily on historical data for process optimization, predictive modeling, and digital twin creation. However, obtaining comprehensive historical datasets can be challenging because of various factors such as equipment upgrades, process changes, and limited data collection. Generative Adversarial Networks (GANs) have emerged as a promising solution to this issue by generating synthetic data that closely resemble real-world distributions. This study explored the application of GANs to generate historical data for semiconductor fabrication facilities. We discuss the importance of data quality, the challenges in modeling complex fab operations, and the need to maintain data privacy. We also examined specialized GAN architectures suited for time-series data generation, multimodal GANs for handling different data types, and hierarchical GANs for capturing relationships between equipment, processes, and fab-wide metrics. Training approaches, evaluation metrics, and potential applications of GAN-generated data in fab simulations, predictive maintenance, and process optimization are also discussed. Furthermore, we address concerns regarding the reliability of the synthetic data, potential biases, and computational requirements. Finally, we highlight future research directions, including the integration of GANs with other AI technologies and the possibility of creating digital twins in entire fab facilities. While challenges remain, GANs present a promising avenue for enhancing semiconductor manufacturing through improved predictive modeling and digital twin creation.