  Ptychography is a powerful imaging technique that is used in a variety of
fields, including materials science, biology, and nanotechnology. However, the
accuracy of the reconstructed ptychography image is highly dependent on the
accuracy of the recorded probe positions which often contain errors. These
errors are typically corrected jointly with phase retrieval through numerical
optimization approaches. When the error accumulates along the scan path or when
the error magnitude is large, these approaches may not converge with
satisfactory result. We propose a fundamentally new approach for ptychography
probe position prediction for data with large position errors, where a neural
network is used to make single-shot phase retrieval on individual diffraction
patterns, yielding the object image at each scan point. The pairwise offsets
among these images are then found using a robust image registration method, and
the results are combined to yield the complete scan path by constructing and
solving a linear equation. We show that our method can achieve good position
prediction accuracy for data with large and accumulating errors on the order of
$10^2$ pixels, a magnitude that often makes optimization-based algorithms fail
to converge. For ptychography instruments without sophisticated position
control equipment such as interferometers, our method is of significant
practical potential.
