用于集成电路三维纳米级成像的加速深度自监督叠层-分层成像技术
Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits
AI总结：
针对集成电路三维纳米检测耗时长的问题，提出物理正则化深度自监督学习架构，实现叠层-分层成像16倍采样减少与4.67倍加速，且重建质量优于全扫描密集采样结果。
摘要：
集成电路等纳米结构的三维检测对保障安全性与可靠性至关重要。该检测需要两种扫描操作：叠层扫描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。旋转扫描有两种可行方式：断层扫描与分层扫描。对于无法实现完整180度覆盖的扁平延展样品，分层扫描更优，因为与有限角断层扫描相比，它能更好地覆盖三维空间频域；同时这也是因为所有投影穿过样品的衰减量大致相同。然而，由于采集与计算量巨大，这两种技术都十分耗时。本研究中，我们采用物理正则化深度自监督学习架构，实现了集成电路叠层-分层成像重建的加速，角度采样数量减少16倍，计算速度提升4.67倍。我们将重建结果与采用全扫描、无学习的密集采样重建结果进行了保真度对比。正如已有研究[Zhou与Horstmeyer，《Opt. Express》，28(9)，第12872-12896页]所报道的，由于自监督学习核具备填补缺失锥的能力，我们观察到重建质量甚至优于密集采样重建结果。
英文摘要：
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.
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