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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.

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 (

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.

Voltage contrast (VC) images obtained using an energy filter (EF) were used to measure the bottom surface of high-aspect- ratio (HAR) structures. The VC images obtained using the conventional EF were sensitive to variations in wafer potential. Since CD-SEM metrology requires precise EF voltage control when using VC images, we developed an EF voltage correction method to be used at each measurement point. Consequently, bottom-edge measurement, independent of the wafer potential fluctuations, was achieved by using the newly developed EF. Our developed technique is effective for CD-SEM metrology using VC images.

Scanning electron microscopes (SEMs) are widely used in various fields and have contributed to advances in nanotechnology. To analyse SEM images, it is necessary to consider the size of the probe electron (PE) beam and the range of PE scattering inside the pattern because they affect edge sharpness in SEM images. As the feature size of the sample drops to less than 10 nm, their effects become even more noticeable. In this study, we utilized Monte-Carlo simulation to clarify the effects of the size of the PE beam and the range of PE scattering for patterns with a line width less than 10 nm. Using a reference pattern with a known cross-sectional shape, we determined the PE beam full-width at half maximum (FWHM) for the simulation by fitting the simulation line profile to the experimental one. The resultant simulation SEM images matched the experimental ones for various PE beam FWHM conditions at acceleration voltages of 500 to 2400 V, even though the pattern dimensions differed from those of the reference pattern. These results indicate that analysis using Monte-Carlo simulation is an effective approach to clarifying SEM image formation and that the optimum observation conditions for patterns with a line width even less than 10 nm can be explored using simulation.