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Background: thin resists, below 30 nm, suffer from reduced imaging contrast and signal-to-noise ratio (SNR). This has an impact on the unbiased Line Width Roughness (uLWR) estimation, which becomes artificially low and hence is inaccurate when the SNR drops below certain limit, while reaching an accurate plateau value at higher SNR. Aim: improve the SNR of Scanning Electron Microscope (SEM) images in order to achieve a more reliable and robust roughness measurements on the thin resist, without having to increase the measurement electron dose. Approach: apply an unsupervised machine learning denoising algorithm to SEM raw images of thin resists on two different underlayers. The images were captured using different frame averaging (4, 8, 16, 32, and 64 frames). A systematic analysis is performed to compare the measurements before and after denoising. Results: after denoising, the SNR is improved, the mean CD stayed unchanged, and the roughness using smaller number of frames got closer to the accurate values, obtained using a larger number of frames. Conclusions: we have demonstrated that the use of a machine learning-based denoising algorithm enhances the SNR of SEM images without changing the mean CD and is beneficial for accurate and robust roughness measurements of thin resist.

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Background: Focus-exposure process window measurement and analysis is an essential function in lithography, but the current geometric approach suffers from several significant deficiencies.Aim: By clearly identifying the problems with the geometric process window approach, a process window measurement and analysis method will be proposed to address these problems. Approach:The probabilistic process window (PPW) proposed here takes metrology uncertainty into account and rigorously calculates the expected fraction of in-spec features based on settings for the best dose/focus and presumed random errors in dose and focus. Using the fraction of in-spec features thus calculated, a much more rigorous determination of the trade-off between exposure latitude and depth of focus (DOF) can be performed. Results:The PPW approach is demonstrated on focus-exposure data generated from a standard extreme ultraviolet lithography process at three different pitches, showing the value of this method. Conclusions:The PPW approach offers clear advantages in accuracy for both DOF determination and the best dose/focus determination. Consequently, its use is preferred both for process development applications and high-volume manufacturing.

As High Numerical Aperture Extreme Ultraviolet Lithography (High NA EUVL) gets ready to step in the integrated circuit manufacturing (ICM) world, more and more work is being devoted to ensuring that all the elements involved in the process, from materials to equipment, will be ready to meet the required specifications when the time comes. One of the most critical pieces in such an ecosystem is the photoresist (PR), the material used to accurately transfer the design to the wafer. In the last years we have observed the introduction of various effective alternative approaches, such as dry metal oxide photoresist. PR always had to meet daunting specifications in terms of resolution, roughness, and sensitivity. However, in the brave new world of High NA EUVL, this is not enough. In fact, as the size of the printed features shrink, it is essential to limit the aspect ratio to avoid pattern collapse. Furthermore, the larger NA will reduce the Depth of Focus (DOF), thus requiring the use of ultra-thin resist films. The direct consequence of that is that the resist thicknesses used nowadays will not be suitable for High NA EUVL, where target thickness is expected to drop down to 20nm or less. Such a dramatic reduction in thickness has the potential to negatively impact, beside printing performances, the quality of the metrology and inspection as well, as discussed in our previous work. In the present work, we study the effects of reducing thickness in the case dry resist using various metrology and inspection techniques, such as Critical Dimension Scanning Electron Microscope (CDSEM), Atomic Force Microscopy (AFM), and e-beam defect inspection. As resist thickness decreases, noise level and image contrast are expected to be reduced, with a potential negative impact on the quality of the CD measurements both in terms of accuracy and precision.

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Background: Measuring and subtracting scanning electron microscope (SEM) noise from a biased measurement of roughness leads to an unbiased roughness measurement. This unbiasing procedure becomes harder as the noise in the image increases. For low image signal-to-noise ratio (SNR) (below about 2), unbiased roughness measurement becomes less reliable.Aim: It is important to understand the mechanism for the sensitivity of unbiased roughness accuracy to linescan SNR to look for ways to improve unbiased roughness measurement for very noisy images.Approach: Using a combination of mathematical analysis, simulations, and experimental data, the role of pixel size and pitch in the SNR sensitivity are explored.Results: All evidence points to the correlation of edge detection noise to true edge position as the cause of the errors in unbiased roughness measurement for very noisy images. For small pitch patterns, changes in feature edge position caused by feature roughness will cause changes to the linescan slope, which in turn changes the sensitivity of edge detection to SEM image noise.Conclusions: Smaller pixel sizes and larger feature sizes are less sensitive to the SNR effects described here. For any algorithm used to measure unbiased roughness, the impact of linescan SNR must be carefully assessed.