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Three-dimensional inspection of nanostructures such as integrated circuits isimportant for security and reliability assurance. Two scanning operations arerequired: ptychographic to recover the complex transmissivity of the specimen;and rotation of the specimen to acquire multiple projections covering the 3Dspatial frequency domain. Two types of rotational scanning are possible:tomographic and laminographic. For flat, extended samples, for which the full180 degree coverage is not possible, the latter is preferable because itprovides better coverage of the 3D spatial frequency domain compared tolimited-angle tomography. It is also because the amount of attenuation throughthe sample is approximately the same for all projections. However, bothtechniques are time consuming because of extensive acquisition and computationtime. Here, we demonstrate the acceleration of ptycho-laminographicreconstruction of integrated circuits with 16-times fewer angular samples and4.67-times faster computation by using a physics-regularized deepself-supervised learning architecture. We check the fidelity of ourreconstruction against a densely sampled reconstruction that uses full scanningand no learning. As already reported elsewhere [Zhou and Horstmeyer, Opt.Express, 28(9), pp. 12872-12896], we observe improvement of reconstructionquality even over the densely sampled reconstruction, due to the ability of theself-supervised learning kernel to fill the missing cone.

Accelerated deep self-supervised ptycho-laminography for three-dimensional nanoscale imaging of integrated circuits