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arXiv:2207.08723 · Machine Learning Science and Technology

Realistic mask generation for matter-wave lithography via machine learning

Johannes Fiedler, Adrià Salvador Palau, Eivind Kristen Osestad, Pekka Parviainen, Bodil Holst

Published May 26, 2023• 0 citations
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Abstract

Abstract Fast production of large-area patterns is crucial for the established semiconductor industry and enables 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 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 the 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.

DOI
10.1088/2632-2153/acd988

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Abstract Fast production of large-area patterns is crucial for the established semiconductor industry and enables 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 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 the 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.
Mask-based pattern generation is a crucial step in microchip production. The next-generation extreme-ultraviolet- (EUV) lithography instruments with a wavelength of 13.5 nm are currently under development. In principle, this should allow patterning down to a resolution of a few nanometers in a single exposure. However, there are many technical challenges, including those due to the very high energy of the photons. Lithography with metastable atoms has been suggested as a cost-effective, less-complex alternative to EUV lithography. The great advantage of atom lithography is that the kinetic energy of an atom is much less than that of a photon for a given wavelength. However, until now no method has been available for making masks for atom lithography that can produce arbitrary, high-resolution patterns. Here we present a solution to this problem. First, traditional binary holography is extended to near-field binary holography, based on Fresnel diffraction. By this technique, we demonstrate that it is possible to make masks that can generate arbitrary patterns in a plane in the near field (from the mask) with a resolution down to the nanometer range using a state-of-the-art metastable-helium source. We compare the flux of this source with that of an established EUV source (NXE:3100, ASML) and show that patterns can potentially be produced at comparable speeds. Finally, we present an extension of the grid-based holography method for a grid of hexagonally shaped subcells. Our method can be used with any beam that can be modeled as a scalar wave, including other matter-wave beams such as helium ions, electrons, or acoustic waves.
Mask based pattern generation with photolithography is a crucial step in microchip production. The next generation Extreme Ultra Violet (EUV) lithography instruments with a wavelength of 13.5 nm is currently under development. In principle this allows patterning down to a few nm resolution in a single exposure. However, there are many technical challenges due to the very high energy of the photons among others. Lithography with metastable atoms has been suggested as a cost effective, less complex alternative to EUV lithography. The big advantage of atom lithography is that the kinetic energy of an atom is much smaller than that of a photon for a given wavelength. However up till now no method has been available for making masks for atom lithography that can produce arbitrary, high resolution patterns. Here we present a solution to this problem. We show that it is possible to make masks based on binary holography that can generate arbitrary patterns down to a few nm resolution using a state of the art metastable helium source. We compare the flux of this source with that of an established EUV source (ASML, NXE:3100) and show that patterns can potentially be produced at comparable speeds. Finally we present an extension of the grid based holography method for a grid of hexagonally shaped subcells. Our method can be used with any wave beam, including other matter wave beams such as helium ions or electrons.
Mask-based pattern generation is a crucial step in microchip production, but there are many technical challenges in scaling current techniques down to nanometer resolution. Lithography using metastable atoms has been suggested as a cost-effective, less complex alternative to conventional techniques, including extreme ultraviolet (EUV) photolithography. This study presents a method based on binary holography that can be used to shape atom beams into arbitrary patterns. Simulations demonstrate the potential for state-of-the-art helium sources to produce patterns with nanometer resolution.
Abstract Fast, large area patterning of arbitrary structures down to the nanometre scale is of great interest for a range of applications including the semiconductor industry, quantum electronics, nanophotonics and others. It was recently proposed that nanometre-resolution mask lithography can be realised by sending metastable helium atoms through a binary holography mask consisting of a pattern of holes. However, these first calculations were done using a simple scalar wave approach, which did not consider the dispersion force interaction between the atoms and the mask material. To access the true potential of the idea, it is necessary to access how this interaction affects the atoms. Here we present a theoretical study of the dispersion force interaction between an atom and a dielectric membrane with a hole. We look at metastable and ground state helium, using experimentally realistic wavelengths (0.05–1 nm) and membrane thicknesses (5–50 nm). We find that the effective hole radius is reduced by around 1–7 nm for metastable helium and 0.5–3.5 nm for ground-state helium. As expected, the reduction is largest for thick membranes and slow atoms.
Due to their very short de Broglie wavelengths, matter waves could be used for high-resolution patterning, competing with e.g.e.g.e.g. e-beam or helium-ion lithography for producing features smaller than 10 nm. Binary holography is presently the only method available for mask-based atom lithography, but its theoretical framework has remained quite limited. The authors present an optimal method for making binary holograms that can shape atom beams arbitrarily, allowing for free selection of the geometrical properties of the generated masks, and the resulting structures.
Abstract Machine learning based on artificial neural networks has emerged as an efficient means to develop empirical models of complex systems. Cold atomic ensembles have become commonplace in laboratories around the world, however, many-body interactions give rise to complex dynamics that preclude precise analytic optimisation of the cooling and trapping process. Here, we implement a deep artificial neural network to optimise the magneto-optic cooling and trapping of neutral atomic ensembles. The solution identified by machine learning is radically different to the smoothly varying adiabatic solutions currently used. Despite this, the solutions outperform best known solutions producing higher optical densities.