  Three-dimensional inspection of nanostructures such as integrated circuits is
important for security and reliability assurance. Two scanning operations are
required: ptychographic to recover the complex transmissivity of the specimen;
and rotation of the specimen to acquire multiple projections covering the 3D
spatial frequency domain. Two types of rotational scanning are possible:
tomographic and laminographic. For flat, extended samples, for which the full
180 degree coverage is not possible, the latter is preferable because it
provides better coverage of the 3D spatial frequency domain compared to
limited-angle tomography. It is also because the amount of attenuation through
the sample is approximately the same for all projections. However, both
techniques are time consuming because of extensive acquisition and computation
time. Here, we demonstrate the acceleration of ptycho-laminographic
reconstruction of integrated circuits with 16-times fewer angular samples and
4.67-times faster computation by using a physics-regularized deep
self-supervised learning architecture. We check the fidelity of our
reconstruction against a densely sampled reconstruction that uses full scanning
and no learning. As already reported elsewhere [Zhou and Horstmeyer, Opt.
Express, 28(9), pp. 12872-12896], we observe improvement of reconstruction
quality even over the densely sampled reconstruction, due to the ability of the
self-supervised learning kernel to fill the missing cone.
