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In DUV lithography, scatterometry enables precise measurement of mask dimensions such as the pitch, linewidth, and sidewall-angle of periodic patterns. However, substantial differences in the optical properties of DUV and EUV masks, such as angular sensitivity and mask 3D effects, makes simply extending existing technologies difficult. Using the EUV reflectometer at Lawrence Berkeley National Labs Center for X-Ray Optics with tunable wavelength and illumination angle, we explore how to extend scatterometry to EUV masks, with particular emphasis on using rigorous simulations and experimental data to quantify the accuracy of sensitive measurements such as sidewall-angle.
Mask scatterometry at EUV wavelengths has benefits but also poses challenges that are not present at DUV wavelengths. The benefits come primarily from using the same wavelength as lithography; due to the severe sensitivity of the multilayer mirror to wavelength, the diffraction patterns obtained at DUV wavelengths from EUV masks would be both highly attenuated and substantially distorted. However, stronger mask 3D effects and the sensitivity of the multilayer to angle of illumination add extra levels of complexity to modeling the spectra of EUV masks that are not present in traditional DUV masks.
We use rigorous FDTD (Finite Difference Time Domain) imaging simulations of patterned EUV multilayer masks to generate a library of spectra including gratings with a range of orientations, pitches, line widths, absorber heights, and side-wall angles under a wide range of illumination wavelengths and angles. We then perform SVD-based dimensionality reduction to find an efficient representation, or dictionary, for the spectra. Using this low-dimensional dictionary, we determine the sampling requirements, i.e. which measurements (angles and wavelengths of illumination) are necessary to measure all parameters of interest to a specified accuracy. We finally acquire experimental spectra of known mask features on the EUV reflectometer using different illumination conditions, and use the dictionary to recover the underlying dimensions of the features.
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