Defect Pattern

Defect pattern analysis focuses on identifying and classifying anomalies in various materials and manufacturing processes, aiming to improve quality control and predict material properties. Current research heavily utilizes machine learning, employing generative models like GANs and VAEs, along with convolutional neural networks and transformers, to analyze images and other data representing defects. These techniques are applied across diverse fields, from semiconductor manufacturing and materials science to automated industrial processes, offering significant potential for enhancing efficiency, reducing waste, and improving product quality. The development of robust and efficient algorithms for defect detection and classification remains a key focus, particularly in addressing challenges posed by limited data and complex defect patterns.