Extracting feature lengths, such as width, depth, and so on, from cross-sectional scanning electron microscopy (SEM) images is an inevitable task in the process development of semiconductor devices. If this extraction task is done manually, the precision of the result depends on the operator’s skill, and this task will be time consuming. We previously proposed a deep-learning-based automated measurement method that combines two image-recognition tasks: (1) semantic segmentation for obtaining the boundaries of each area (mask, substrate, and background) and (2) object detection for determining the coordinates of each unit of a line/space (L/S) pattern. However, it required annotation data consisting of segmented images and bounding boxes, which are not easily made by operators. In this study, we propose a novel measurement method based on a human-pose estimation (HPE) model, which is easier to use.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.