The field of semiconductor manufacturing and computer vision is witnessing significant advancements with the integration of innovative techniques such as physics-constrained adaptive neural networks and coarse-to-fine frameworks. These approaches enable real-time optimization, high-precision segmentation, and improved metrology precision, addressing long-standing challenges in the industry. Notably, the application of physics-constrained learning and feature-guided attention mechanisms has led to substantial improvements in segmentation accuracy and generalization capabilities. Furthermore, the development of novel frameworks for segmenting anything at any granularity and upscaling features has expanded the potential of vision foundation models. Noteworthy papers include: LithoSeg, which proposes a coarse-to-fine network for high-precision lithography segmentation, outperforming previous approaches in both segmentation accuracy and metrology precision. Physics-Constrained Adaptive Neural Networks Enable Real-Time Semiconductor Manufacturing Optimization with Minimal Training Data, which presents a physics-constrained adaptive learning framework that achieves consistent sub-nanometer precision using minimal training data. FGNet, which leverages feature-guided attention to refine SAM2 for 3D EM neuron segmentation, achieving performance comparable to state-of-the-art approaches. UnSAMv2, which enables segment anything at any granularity without human annotations, substantially enhancing SAM-2 across interactive, whole-image, and video segmentation tasks. Upsample Anything, which presents a simple and efficient test-time optimization framework for feature upsampling, achieving state-of-the-art performance on semantic segmentation, depth estimation, and probability map upsampling.