As a follow-up to last year’s “What is prevalent CD-SEM's role in EUV era?” [1], here we report our ongoing progress on total metrology solutions for the sub-10-nm extreme ultraviolet (EUV) lithography process. We discuss two technical approaches that have emerged following our previous work. First, similar to conventional minimization processes, we focus on improvements in the top metrology task, down-to-ångström-order tool matching, namely, “atomic matching”, which is a crucially important feature in all in-line metrology tools in the EUV era. Second, we examine a comprehensive solution that enables EUV-characterized featured process monitoring with greater accuracy, higher speed, and smarter metrology.
Projection lithography using extreme ultra-violet (EUV) light at 13.5-nm wavelength will be applied to the production of integrated circuits below 7 nm design-rules. In pursuit of further miniaturization, however, stochastic pattern defect problems have arisen, and monitoring such defect generation probabilities in extremely low range (<10-10) is indispensable. Here, we discuss a new method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. Based on our previously introduced probabilistic model of stochastic pattern defect, the defect probability is expressed as product sum of the probability for edge position and the probability that film defect covers the area between edges, and we describe the later as a function of edge position. The defect probabilities in the order between 10-7 ~ 10-5 were predicted from 105 measurement data for real EUV exposed wafers, suggesting the effectiveness of the model and its potential for defect inspection.
The manuscript version of this Poster Presentation can be viewed in the Journal of Micro/Nanolithography, MEMS, and MOEMS Vol. 18 · No. 2: https://doi.org/10.1117/1.JMM.18.2.024002
Projection lithography using extreme ultraviolet (EUV) light at 13.5-nm wavelength will be applied to the production of integrated circuits below 7-nm design rules. In pursuit of further miniaturization, however, stochastic pattern defect problems have arisen, and monitoring such defect generation probabilities in extremely low range (<10 − 10) is indispensable. We discuss a method for predicting stochastic defect probabilities from a histogram of feature sizes for patterns several orders of magnitude fewer than the number of features to inspect. Based on our previously introduced probabilistic model of stochastic pattern defect, the defect probability is expressed as the product sum of the probability for edge position and the probability that film defect covers the area between edges, and we describe the latter as a function of edge position. The defect probabilities in the order between 10 − 7 and 10 − 5 were predicted from 105 measurement data for real EUV-exposed wafers, suggesting the effectiveness of the model and its potential for defect inspection.
Measurement of line edge roughness (LER) is discussed from four aspects: edge detection, power spectrum densities (PSD) prediction, sampling strategy, and noise mitigation. General guidelines and practical solutions for LER measurement today are introduced. Advanced edge detection algorithms such as the wave-matching method are shown to be effective for robustly detecting edges from low SNR images, whereas a conventional algorithm with weak filtering is still effective in suppressing SEM noise and aliasing. An advanced PSD prediction method such as the multitaper method is effective in suppressing sampling noise within a line edge to analyze, whereas a number of lines are still required for suppressing line-to-line variation. Two types of SEM noise mitigation methods, such as the “apparent noise floor” subtraction method and LER-noise decomposition using regression analysis, are verified to successfully mitigate SEM noise from PSD curves. These results are extended to local critical-dimension uniformity (LCDU) measurement to clarify the impact of SEM noise and sampling noise on LCDU.
KEYWORDS: Line edge roughness, Scanning electron microscopy, Edge detection, Signal to noise ratio, Critical dimension metrology, Detection and tracking algorithms, Metrology, Reliability, Process control, Image filtering
Measurement of line edge roughness (LER) is discussed from four aspects: edge detection, PSD prediction, sampling strategy, and noise mitigation, and general guidelines and practical solutions for LER measurement today are introduced. Advanced edge detection algorithms such as wave-matching method are shown effective for robustly detecting edges from low SNR images, while conventional algorithm with weak filtering is still effective in suppressing SEM noise and aliasing. Advanced PSD prediction method such as multi-taper method is effective in suppressing sampling noise within a line edge to analyze, while number of lines is still required for suppressing line to line variation. Two types of SEM noise mitigation methods, "apparent noise floor" subtraction method and LER-noise decomposition using regression analysis are verified to successfully mitigate SEM noise from PSD curves. These results are extended to LCDU measurement to clarify the impact of SEM noise and sampling noise on LCDU.
Fingerprint edge roughness (FER) is proposed to characterize high frequency roughness of fingerprint pattern edges assembled by lamella forming block copolymer (BCP). The FER is a roughness index which does not include the roughness component of the fingerprint curvature. A technique to evaluate FER by using CD-SEM is also proposed. Centerline of the fingerprint patterns were extracted by utilizing binarization and slimming algorithm, and line width, line width roughness and line edge roughness along the centerline were measured. The FER thus measured showed a good agreement with those determined by utilizing conventional line edge roughness analyzing algorithm. The FERs of fingerprint patterns assembled with various BCP formulations were analyzed. As a result, the proposed technique successfully detected the line edge roughness difference between each BCP formulations with different compositions. The results indicate that the FER might be a useful index to evaluate the patterning performance of BCP as a material for DSA process. The proposed technique will provide a method for fast and easy development of BCP materials and processes
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