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Advanced high-voltage e-beam system combined with an enhanced D2DB for on-device overlay measurement
We propose here an alternative path to investigate and discriminate the root causes of LWR using only wafer data. It is based on Local Critical Dimension Uniformity (LCDU) decomposition [2], a methodology used to identify and quantify the individual LCDU contributors. The decomposition approach requires a smart sampling of the wafer print, in which an array of contact hole is measured in different dies multiple times. For such an approach to be successful, it is critical to ensure that the measurement locations are individually identified. Hence, it is necessary to anchor the metrology to a reference feature. A linear nested model [3] is then used to quantify the three main variability components (mask, shot noise, and metrology). This approach allows to sample thousands of features at mask, a task that would not be practically achievable through direct mask measurements.
In this work, LWR decomposition is implemented for the first time. To this aim, 18nm lines at 36nm pitch, printed by EUV lithography, were used. We specifically worked with a pattern including programmed defects, used as anchoring features for the metrology. In order to limit the impact of the metrology noise, expected to be higher for lines as compared to CH, we sampled over 8000 anchored measurements per image (in the CH case, only 81 measurements per image were needed). The LWR decomposition results indicated the dominance of the metrology noise, as expected. In addition, the mask contribution was observed to be less relevant that the shot noise.
To verify the accuracy of the LWR decomposition results, Power Spectral Density (PSD) analysis on wafer and mask SEM images was used. The metrology noise contribution was removed at both mask and wafer level using an un-biasing normalization of the PSD curves [4]. The comparison with the PSD analysis confirmed the feasibility of LWR decomposition, opening the way to a more effective diagnostic technique for roughness and stochastics.
Aim: Unbiasing of roughness measurements is best accomplished by taking advantage of the frequency characteristics of the noise to measure and subtract it out. This requires the ability to detect edges in a noisy SEM image without the use of standard image filtering techniques
. Approach: A physics-based inverse linescan model is used to robustly detect edges in high-noise SEM images without the use of filtering or image averaging. To validate the efficacy of SEM noise measurement and subtraction, rough features were measured under a wide variety of SEM settings, including number of frames of averaging and voltage.
Results: In all cases, the vast majority of the measurement bias was properly subtracted out. Over a wide range of SEM settings the biased roughness varied by more than a factor of two, but the unbiased linewidth roughness varied by only a few percent.
Conclusions: The approach of inverse-linescan edge detection followed by noise measurement and subtraction leads to reliable estimates of the true (unbiased) line-edge and linewidth roughness of features on the wafer. These unbiased estimates are quite insensitive to metrology tool settings over a reasonable range of values.
The first comes from the ongoing scaling down of linewidths, which dictates SEM imaging with less scanning frames to reduce specimen damage and hence with more noise. During the last decade, it has been shown that image noise can be an important budget of the measured LER while systematically affects and alter the PSD curve of LER at high frequencies. A recent method for unbiased LER measurement is based on the systematic Fourier or correlation analysis to decompose the effects of noise from true LER (Fourier-Correlation filtering method). The success of the method depends on the PSD and HHCF curve. Previous experimental and model works have revealed that etch transfer affects the PSD of LER reducing its high frequency values. In this work, we estimate the noise contribution to the biased LER through PSD flat floor at high frequencies and relate it with the differences between the PSDs of lithography and etched LER. Based on this comparison, we propose an improvement of the PSD/HHCF-based method for noise-free LER measurement to include the missed high frequency real LER.
The second issue is related with the increased density of lithographic patterns and the special characteristics of DSA and MP lithography patterns exhibits. In a previous work, we presented an enlarged LER characterization methodology for such patterns, which includes updated versions of the old metrics along with new metrics defined and developed to capture cross-edge and cross-line correlations. The fundamental concept has been the Line Center Roughness (LCR), the edge c-factor and the line c-factor correlation function and length quantifying the line fluctuations and the extent of cross-edge and cross-line correlations. In this work, we focus on the role of etch steps on cross-edge and line correlation metrics in SAQP data. We find that the spacer etch steps reduce edge correlations while etch steps with pattern transfer increase these. Furthermore, the density doubling and quadrupling increase edge correlations as well as cross-line correlations.
To be effective during the lithographic EUV material screening phase for such tight pitches, it is necessary to implement complementary metrology analyses that can provide precise information on the resist roughness and a quick feedback on the quantification of nano-failures (nano-bridges, broken lines, merging or missing contacts) induced by a stochastic EUV patterning regime, the random nature of the light-matter interaction and consequent chemical reactions. Beside the traditional approach to characterize a resist with metrics as exposure latitude (EL%), depth of focus (DoF) and line-edge-roughness (LER) based on CDSEM measurements, we have used the power spectra density (PSD) [4] to get an unbiased value of the resist line roughness (LWR and LER) by using Fractilia metroLERTM commercial software. Further, we have used Stochalis imec software [5] to quantify patterning nano failures providing an early stage assessment on the patterning fidelity of the examined resists.
We present the resist characterization results for 32nm dense line-space pattern on different substrates and for 36nm dense and orthogonal contact hole pitch pattern for different photoresists. Two positive tone chemically amplified (CA) resists have been identified at the exposure dose of 45mJ/cm2 and 33mJ/cm2 for logic (pitch 32nm dense line/space) and memory (pitch 36nm dense contact holes) use cases, respectively.
We observed that the net effect of the etch process is to improve LCDU, although the final LCDU is both stack- and resist-dependent. Different resists demonstrate the same LCDU improvement, so that the LCDU after etch will depend on the initial resist performance. Using a stack different from the one used to set up the etch process can undermine the LCDU improvement.
The impact of the various etch steps is investigated in order to identify the physical mechanisms responsible for the LCDU improvement through etch. Both top-down and cross section Scanning Electron Microscopy (SEM) are used. The step-by-step analysis of the etch process showed that the main LCDU improvement is achieved during oxide etch, while the other process steps are either ineffective or detrimental in terms of LCDU. The main cause of the LCDU improvement is then attributed to the polymerization of the CH surface happening during the oxide etch.
Finally, the LCDU improvement caused by the etch process is investigated as a function of the initial LCDU after litho in a relatively broad range (2-15nm). The ratio between LCDU after litho over LCDU after etch is investigated as a function of the initial LCDU after litho for two different resists. The results indicate that the impact of etch on LCDU is characterized by a single curve, specific to the etch process in use and independent of the resist type. In addition, we observe that the percentage LCDU improvement is constant above a certain threshold, in agreement with the throughpitch results.
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