Substrate defect-detection and comparison

Abstract

Various examples described herein include image-processing tasks of image data used for defect detection and comparison of features on substrates. At least one of a deep-convnet-based and a transformer-based backbone network (a common backbone) that is arranged to convert various types of raw-image data into features that can, in turn, be used by smaller neural networks to perform final calculations of specific tasks. The smaller neural networks perform, for example, final defect-detections, die-to-die image comparisons, anomaly detection, and customer-specific tasks in a fabrication facility. The common-backbone network can initially be trained using self-supervised learning based on the raw-image data, and then transfer learning can be used to train a final application of the task-specific networks. Other systems and methods are also disclosed.