Advanced EUV mask and imaging modeling

J. Micro/Nanopattern. Mats. Metro.

Bottiglieri

et al. 2021

Self Cite

Background: Explaining imaging phenomena in EUV lithography requires more than a single point of view. Traditionally, the diffraction characteristics of EUV masks are analyzed in terms of the amplitude and phase of diffraction orders that are generated by the absorber pattern.Aim: We propose a complementary perspective to view the EUV mask absorber openings as waveguides.Approach: Comparisons between RCWA simulations and analytical solutions to waveguide equations are performed to prove that EUV mask absorbers behave as a waveguide.Results: This perspective can explain phenomena left unexplained by conventional analysis of far-field diffraction orders. Conclusions:The waveguiding effect in EUV mask absorbers explains the need for low refractive index and high extinction materials. The waveguide perspective explains why attenuated phase shift masks behave differently for EUV than our traditional understanding would suggest.

“…In EUV, the light passes through the absorber twice and exhibits double diffraction. [7][8][9] The schematic shown in Fig. 16 shows a mask model 8 with a thick absorber and a multilayer.…”

Section: Interaction Between Waveguide Modes and Diffraction Ordersmentioning

confidence: 99%

Section: Interaction Between Waveguide Modes and Diffraction Ordersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Investigation of waveguide modes in EUV mask absorbers

Mesilhy

J. Micro/Nanopattern. Mats. Metro.

Bottiglieri

et al. 2021

Self Cite

“…Consequently, the next task to be solved is the transformation of the detected defective patterns in the SEM images into 3D mask structures and furthermore into a specific Manhattanized format required by the simulator. 48 This transformation needs advanced image processing in combination with additional information about the mask and mask materials to allow for the 3D extension of the data. After that, the determined defect shape is used to mimic a repair, which requires further processing of geometry data.…”

Section: Sem Images With Contact Patterns 64mentioning

confidence: 99%

Mask defect detection with hybrid deep learning network

J. Micro/Nanopattern. Mats. Metro.

Auth

Heil

et al. 2021

Self Cite

Background: Deep learning is a very fast-growing field in the area of artificial intelligence with remarkable results in recent years. Many works in the lithography and photomask field have shown progress of technology in this application area and the potential to improve lithography by the automation of processes. Despite this progress, the use of machine learning techniques in the field of mask repair still seems to be at the beginning.Aim: We show that deep learning-based methods can successfully be applied to mask repair applications.Approach: The presented system is a hybrid and modular approach based on a combination of several deep learning networks and analytical methods, enabling the detection of mask pattern defects and the determination of the exact defect shapes from SEM images. In the current version, the system is trained for line/space patterns and contact patterns with typical defect types. The modularity allows for the extensibility to new use cases. The issue of an insufficient amount of training data is addressed using purely computer-generated simplified SEM data in combination with a specific network architecture. Results:The very good functionality and defect detection accuracy of the system are demonstrated with a set of real SEM images with line/space patterns and contact patterns with numerous defects. In particular, a 100% true defect detection rate could be obtained. Conclusions:The presented machine learning approach demonstrates the successful defect identification, location, and shape determination from real mask SEM images.

“…The mask and imaging modeling in Dr. LiTHO combines the Abbe method, i.e., the image computation for a discrete set of source points, with rigorous computation of light diffraction for representative illumination directions. 34 For the purpose of this study about 100 source points and five representative illumination directions per pole were used. The good accuracy of the image models in Dr. LiTHO was verified by comparisons to other simulation tools, especially Hyperlith.…”

Section: Parametric Analysis Of Selected Critical Use Casesmentioning

confidence: 99%

Perspectives and tradeoffs of absorber materials for high NA EUV lithography

Erdmann

Mesilhy

J. Micro/Nanolith. MEMS MOEMS

et al. 2020

Self Cite

Next-generation extreme ultraviolet (EUV) systems with numerical apertures of 0.55 have the potential to provide sub-8-nm half-pitch resolution. The increased importance of stochastic effects at smaller feature sizes places further demands on scanner and mask to provide high contrast images. We use rigorous mask diffraction and imaging simulation to understand the impact of the EUV mask absorber and to identify the most appropriate optical parameters for high NA EUV imaging. Simulations of various use cases and material options indicate two main types of solutions: high extinction materials, especially for lines spaces, and low refractive index materials that can provide phase shift mask solutions. EUV phase masks behave very different from phase shift masks for DUV. Carefully designed low refractive index materials and masks can open up a new path toward high contrast edge printing. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.