High-resolution limited-angle phase tomography of dense layered objects using deep neural networks

We demonstrate that it is possible to use deep neural networks to produce tomographic reconstructions of dense layered objects with small illumination angle as low as 10 . It is also shown that a DNN trained on synthetic data can generalize well to and produce reconstructions from experimental measurements. This work has application in the field of X-ray tomography for the inspection of integrated circuits and other materials studies. We present a machine learning-based method for tomographic reconstruction of dense layered objects, with range of projection angles limited to 10 . Whereas previous approaches to phase tomography generally require 2 steps, first to retrieve phase projections from intensity projections and then to perform tomographic reconstruction on the retrieved phase projections, in our work a physics-informed preprocessor followed by a deep neural network (DNN) conduct the 3-dimensional reconstruction directly from the intensity projections. We demonstrate this single-step method experimentally in the visible optical domain on a scaled-up integrated circuit phantom. We show that even under conditions of highly attenuated photon fluxes a DNN trained only on synthetic data can be used to successfully reconstruct physical samples disjoint from the synthetic training set. Thus, the need for producing a large number of physical examples for training is ameliorated. The method is generally applicable to tomography with electromagnetic or other types of radiation at all bands. Tomography is the quintessential inverse problem.