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Fast production of large area patterns with nanometre resolution is crucialfor the established semiconductor industry and for enabling industrial-scaleproduction of next-generation quantum devices. Metastable atom lithography withbinary holography masks has been suggested as a higher resolution/low-costalternative to the current state of the art: extreme ultraviolet (EUV)lithography. However, it was recently shown that the interaction of themetastable atoms with the mask material (SiN) leads to a strong perturbation ofthe wavefront, not included in existing mask generation theory, which is basedon classical scalar waves. This means that the inverse problem (creating a maskbased on the desired pattern) cannot be solved analytically even in 1D. Here wepresent a machine learning approach to mask generation targeted for metastableatoms. Our algorithm uses a combination of genetic optimisation and deeplearning to obtain the mask. A novel deep neural architecture is trained toproduce an initial approximation of the mask. This approximation is then usedto generate the initial population of the genetic optimisation algorithm thatcan converge to arbitrary precision. We demonstrate the generation of arbitrary1D patterns for system dimensions within the Fraunhofer approximation limit.

Realistic mask generation for matter-wave lithography via machine learning