Stochastic Defect

Stochastic defects, unpredictable imperfections in materials or processes, pose significant challenges across diverse fields, from semiconductor manufacturing to medical imaging. Current research focuses on generating synthetic defect data using advanced techniques like diffusion models and generative adversarial networks (GANs) to address data scarcity and improve the performance of defect detection algorithms. These methods, often incorporating disentangled representations and consistency modeling, aim to create realistic and diverse synthetic defects for training robust and accurate models. The improved detection and classification of stochastic defects have substantial implications for quality control, process optimization, and ultimately, the reliability and performance of various technologies.