Synthetic Wafer Test Data Generation – Principles, Methods, and Validation

Authors

Tarun Parmar

Abstract

Wafer test data plays a crucial role in semiconductor manufacturing, enabling defect identification, process optimization, and yield improvement. However, acquiring real-world data presents challenges, such as data scarcity, privacy concerns, and high costs. Synthetic data generation has emerged as a promising solution that offers increased data availability, privacy preservation, cost-effectiveness, and flexibility. This study explores the principles, methods, and validation techniques for generating synthetic wafer test data. Key techniques include randomized sampling with variability modeling to introduce controlled randomness, spatial modeling using Gaussian processes and Markov random fields for realistic defect map generation, and physics-based simulations incorporating semiconductor physics principles. Generative AI techniques such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are discussed, highlighting their suitability for different wafer test data types. GANs excel in visual inspection tasks, whereas VAEs are well suited for parametric testing and anomaly detection. The validation and evaluation of synthetic data quality are crucial, emphasizing the importance of preserving statistical similarity, correlations, and improving downstream tasks. The metrics and methods for assessing data quality, including statistical tests, visual inspections, and domain-specific metrics, are discussed. The potential for synthetic data to revolutionize semiconductor manufacturing by enhancing decision making, optimizing yields, and driving innovation. Future research directions include refining generative models, developing sophisticated validation techniques, and exploring hybrid modeling approaches that integrate synthetic and real-world data.

Keywords

Synthetic Data Generation, Wafer Test Data, Semiconductor Manufacturing, Generative AI Models, Data Validation

Synthetic Wafer Test Data Generation – Principles, Methods, and Validation. Tarun Parmar. 2024. IJIRCT, Volume 10, Issue 4. Pages 1-5. https://www.ijirct.org/viewPaper.php?paperId=2501115