Semiconductor Datasets

Semiconductor datasets are crucial for advancing materials science and manufacturing, focusing on efficient defect detection and accurate material characterization. Current research emphasizes overcoming challenges like high dimensionality, missing data (up to 90% in some cases), and class imbalance in defect datasets, employing techniques such as generative models (e.g., diffusion models), graph-based causal discovery methods, and convolutional neural networks for improved defect detection and classification. These advancements are vital for optimizing semiconductor manufacturing processes, enhancing yield, and accelerating the development of novel materials with improved properties.