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：三维纳米结构（例如集成电路）的检查对于安全性和可靠性保障至关重要。需要进行两个扫描操作：ptychographic 扫描用于恢复样品的复杂透射率；以及样品旋转扫描来获取覆盖 3D 空间频率域的多个投影。有两种类型的旋转型扫描：层析成像和层压扫描。对于扁平的、延伸的样品，由于完全覆盖 180 度不可能，因此后者更加优选，因为相对有限角度层析成像而言，能够提供更好的 3D 空间频率域的覆盖，并且因为通过样品的吸收量在所有投影中大致相同。然而，这两种技术由于广泛的采集和计算时间而需要耗费大量时间。本文中，我们演示了使用物理正则化的深度自我监督学习架构加速集成电路的ptycho-laminographic重建，采用16倍较少的角度样品，计算速度比无学习速度快4.67倍。我们根据我们的重建结果与使用全扫描而没有修正的稠密重建进行了真实性检查。正如在其他地方(Zhou and Horstmeyer, Opt. Express, 28(9), pp. 12872-12896)已经报道的那样，由于自我监督学习核可以填补丢失的圆锥形区域，我们观察到重建质量即使超越稠密采样重建，其质量也有所提高。