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.

翻译：集成电路等纳米结构的三维检测对安全性和可靠性保障至关重要。需要两种扫描操作：叠层成像以恢复样品的复透射率；以及样品旋转以获取覆盖三维空间频域的多个投影。存在两种旋转扫描方式：断层成像和层析成像。对于无法实现180度全覆盖的扁平大尺寸样品，后者更为优选，因其相比有限角度断层成像能提供更好的三维空间频域覆盖。同时，所有投影穿过样品的衰减量也大致相同。然而，这两种技术均因数据采集和计算耗时较长而效率低下。本文通过采用物理正则化深度自监督学习架构，实现了叠层层析成像重建的加速：角度采样数减少16倍，计算速度提高4.67倍。我们将重建结果与采用全扫描且无学习的密集采样重建进行保真度对比。如已有文献[Zhou and Horstmeyer, Opt. Express, 28(9), pp. 12872-12896]所述，由于自监督学习核能够填充缺失锥体，我们观察到重建质量甚至优于密集采样重建。