  Noninvasive X-ray imaging of nanoscale three-dimensional objects, e.g.
integrated circuits (ICs), generally requires two types of scanning:
ptychographic, which is translational and returns estimates of complex
electromagnetic field through ICs; and tomographic scanning, which collects
complex field projections from multiple angles. Here, we present Attentional
Ptycho-Tomography (APT), an approach trained to provide accurate
reconstructions of ICs despite incomplete measurements, using a dramatically
reduced amount of angular scanning. Training process includes regularizing
priors based on typical IC patterns and the physics of X-ray propagation. We
demonstrate that APT with 12-time reduced angles achieves fidelity comparable
to the gold standard with the original set of angles. With the same set of
reduced angles, APT also outperforms baseline reconstruction methods. In our
experiments, APT achieves 108-time aggregate reduction in data acquisition and
computation without compromising quality. We expect our physics-assisted
machine learning framework could also be applied to other branches of nanoscale
imaging.

Image and Video Processing;Machine Learning;Optics