X-ray computed laminographic tomography (CLT) is a viable tool for creating high-throughput volumetric imaging of large, planar samples. In this work, we present a self-supervised deep image restoration workflow to produce noise-free, artifact-free volumetric reconstructions for laminographic tomography. We demonstrate our CLT method on a variety of samples scanned with an in-house prototype system, showing that our proposed method notably outperforms classic reconstruction methods, that has the potential for more accurate detection of defects and estimation of critical dimensions, thereby providing a feasible solution for rapid inline inspection and failure analysis in advanced integrated circuits packaging.
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.