Segment Anything Model for Grain Characterization in Hard Drive Design

The high-throughput quickly changing of the grains, but rule-based segmentation models fail due research environment makes zero-shot generalization an to weak grain boundary contrast and contrast fluctuations incredibly desirable feature. For this reason, we explore across the image. Previous work has gotten around this the application of Meta's Segment Anything Model (SAM) problem by training neural networks on hundreds of handlabeled to this problem. We first analyze the out-of-the-box use of grains [12, 18]. This method works well for segmenting SAM. Then we discuss opportunities and strategies for improvement grain images if the material and image conditions under the assumption of minimal labeled data are not changing, but in the fast-paced semiconductor availability. Out-of-the-box SAM shows promising accuracy research environment, processing conditions and material at property distribution extraction. We are able to identify composition may be changing on even a wafer-to-wafer basis.