Realistic mask generation for matter-wave lithography via machine learning

TL;DR

This paper introduces a machine learning method combining deep neural networks and genetic algorithms to generate masks for metastable atom lithography, addressing wavefront perturbation challenges and enabling arbitrary pattern creation.

Contribution

It presents a novel ML-based approach for mask generation in metastable atom lithography, overcoming limitations of classical theory and enabling high-precision pattern design.

Findings

Successfully generated arbitrary 1D patterns within Fraunhofer limit

Combined deep learning and genetic optimization improves mask accuracy

Addresses wavefront perturbation effects in mask design

Abstract

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