US-12620550-B2 - Method of performing metrology on a microfabrication pattern

US12620550B2US 12620550 B2US12620550 B2US 12620550B2US-12620550-B2

Abstract

A method includes generating, by a SEM, sets of frames corresponding to regions of a microfabrication pattern, for each set of frames, estimating feature data representing edge positions, linewidths, or centerline positions of one or more features of each region of the pattern, and computing a preliminary estimate of a roughness parameter from the feature data. The roughness parameter is indicative of a line edge roughness, a linewidth roughness, or a pattern placement roughness of the one or more features. The method further includes fitting a model equation to the preliminary estimates of the roughness parameter using a model parameter dependent on the number of frames of each set of frames, the model equation relating the model parameter to the roughness parameter; and computing a final estimate of the roughness parameter as an asymptotic value of the fitted model equation.

Inventors

Joren Severi
Gian Francesco Lorusso
Danilo De Simone

Assignees

IMEC VZW
KATHOLIEKE UNIVERSITEIT LEUVEN

Dates

Publication Date: 20260505
Application Date: 20230315
Priority Date: 20220316

Claims (20)

1 . A method comprising: transmitting control parameters to a scanning electron microscope; generating, by the scanning electron microscope and according to the control parameters, a first set of frames of a first region of a microfabrication pattern, a second set of frames of a second region of the microfabrication pattern, and a third set of frames of a third region of the microfabrication pattern, wherein a number of frames of the first set, the second set and the third set are different; estimating, using the first set, the second set, and the third set, first feature data corresponding to the first set, second feature data corresponding to the second set, and third feature data corresponding to the third set, wherein the first feature data, the second feature data, and the third feature data represent edge positions, linewidths, or centerline positions of features of the microfabrication pattern; computing a first preliminary estimate, a second preliminary estimate, and a third preliminary estimate of a roughness parameter from the first feature data, the second feature data, and the third feature data, wherein the roughness parameter is indicative of a line edge roughness, a linewidth roughness, or a pattern placement roughness of the features of the microfabrication pattern; fitting a model equation to the first preliminary estimate, the second preliminary estimate, and the third preliminary estimate using a model parameter that is dependent on the number of frames of the first set, the second set, or the third set, the model equation relating the model parameter to the roughness parameter, wherein the model parameter is an average signal-to-noise ratio for the first set, the second set, or the third set; and computing a final estimate of the roughness parameter using the model equation.
2 . The method according to claim 1 , wherein the roughness parameter is one or more standard deviations of an edge position of the microfabrication pattern.
3 . The method according to claim 1 , wherein the roughness parameter is one or more standard deviations of a linewidth of the microfabrication pattern.
4 . The method according to claim 1 , wherein the roughness parameter is one or more standard deviations of a centerline position of the microfabrication pattern.
5 . The method according to claim 1 , wherein the first preliminary estimate, the second preliminary estimate, and the third preliminary estimate of the roughness parameter are noise-unbiased preliminary estimates of the roughness parameter.
6 . The method according to claim 5 , further comprising: computing a spatial frequency density representation from the first feature data, and estimating a noise floor of the spatial frequency density representation, wherein computing the first preliminary estimate comprises computing the first preliminary estimate using the spatial frequency density representation and the noise floor.
7 . The method according to claim 6 , wherein computing the first preliminary estimate further comprises: computing a noise-unbiased spatial frequency density representation by subtracting the noise floor from the spatial frequency density representation; and integrating the noise-unbiased spatial frequency density representation.
8 . The method according to claim 6 , wherein computing the first preliminary estimate further comprises: integrating the spatial frequency density representation to obtain a noise-biased estimate of the roughness parameter; and subtracting the noise floor from the noise-biased estimate.
9 . The method according to claim 6 , wherein the spatial frequency density representation is a Fourier spectrum or a power spectral density.
10 . The method according to claim 1 , further comprising, generating a first composite image as a pixel-wise average of the first set of frames, a second composite image as a pixel-wise average of the second set of frames, and a third composite image as a pixel-wise average of the third set of frames, wherein estimating the first feature data, the second feature data, and the third feature data comprises estimating the first feature data using the first composite image, estimating the second feature data using the second composite image, and estimating the third feature data using the third composite image.
11 . The method according to claim 1 , wherein the model parameter is equal to the number of frames of the first set, the second set, or the third set.
12 . The method according to claim 1 , wherein the model equation is an exponential function.
13 . The method according to claim 12 , wherein the model equation is Y=a(1−b*e c*x ), wherein Y is the roughness parameter, x is the model parameter, and a, b, care fitting parameters.
14 . The method according to claim 1 , wherein the number of frames of the first set is less than 8, and wherein the numbers of frames of the second set and the third set are each equal to or greater than 8.
15 . The method according to claim 1 , wherein the microfabrication pattern is a resist pattern formed on a substrate.
16 . The method according to claim 1 , wherein the microfabrication pattern is an etched pattern formed on a substrate.
17 . The method of claim 1 , wherein transmitting the control parameters comprises transmitting the control parameters such that the control parameters comprise one or more of a pixel size, a beam current, a frame resolution, or a number of frames to be integrated.
18 . The method of claim 1 , wherein transmitting the control parameters comprises transmitting the control parameters such that the control parameters comprise coordinates indicating the first region, the second region, and the third region.
19 . A non-transitory computer-readable media storing instructions that, when executed by a computing device, cause the computing device to perform functions comprising: transmitting control parameters to a scanning electron microscope; generating, by the scanning electron microscope and according to the control parameters, a first set of frames of a first region of a microfabrication pattern, a second set of frames of a second region of the microfabrication pattern, and a third set of frames of a third region of the microfabrication pattern, wherein a number of frames of the first set, the second set and the third set are different; estimating, using the first set, the second set, and the third set, first feature data corresponding to the first set, second feature data corresponding to the second set, and third feature data corresponding to the third set, wherein the first feature data, the second feature data, and the third feature data represent edge positions, linewidths, or centerline positions of features of the microfabrication pattern; computing a first preliminary estimate, a second preliminary estimate, and a third preliminary estimate of a roughness parameter from the first feature data, the second feature data, and the third feature data, wherein the roughness parameter is indicative of a line edge roughness, a linewidth roughness, or a pattern placement roughness of the features of the microfabrication pattern; fitting a model equation to the first preliminary estimate, the second preliminary estimate, and the third preliminary estimate using a model parameter that is dependent on the number of frames of the first set, the second set, or the third set, the model equation relating the model parameter to the roughness parameter, wherein the model parameter is an average signal-to-noise ratio for the first set, the second set, or the third set; and computing a final estimate of the roughness parameter using the model equation.
20 . A computing device comprising: a processor; and a computer readable medium storing instructions that, when executed by the processor, cause the computing device to perform functions comprising: transmitting control parameters to a scanning electron microscope; generating, by the scanning electron microscope and according to the control parameters, a first set of frames of a first region of a microfabrication pattern, a second set of frames of a second region of the microfabrication pattern, and a third set of frames of a third region of the microfabrication pattern, wherein a number of frames of the first set, the second set and the third set are different; estimating, using the first set, the second set, and the third set, first feature data corresponding to the first set, second feature data corresponding to the second set, and third feature data corresponding to the third set, wherein the first feature data, the second feature data, and the third feature data represent edge positions, linewidths, or centerline positions of features of the microfabrication pattern; computing a first preliminary estimate, a second preliminary estimate, and a third preliminary estimate of a roughness parameter from the first feature data, the second feature data, and the third feature data, wherein the roughness parameter is indicative of a line edge roughness, a linewidth roughness, or a pattern placement roughness of the features of the microfabrication pattern; fitting a model equation to the first preliminary estimate, the second preliminary estimate, and the third preliminary estimate using a model parameter that is dependent on the number of frames of the first set, the second set, or the third set, the model equation relating the model parameter to the roughness parameter, wherein the model parameter is an average signal-to-noise ratio for the first set, the second set, or the third set; and computing a final estimate of the roughness parameter using the model equation.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The present application is a non-provisional patent application claiming priority to European Patent Application No. 22162539.5, filed on Mar. 16, 2022, the contents of which are hereby incorporated by reference. FIELD OF THE DISCLOSURE The present disclosure relates to a method of performing metrology on a microfabrication pattern, and further to a computer-readable media configured to implement a corresponding method. BACKGROUND Over the past decades, integrated circuits have seen a continuous increase of computing power by including increasing numbers of transistors per unit area. The semiconductor industry has witnessed the transition from contact and proximity printing in the 1970s to a range of projection lithography techniques. The drastic resolution improvement in projection lithography has been enabled by three main factors: (1) the reduction of imaging source wavelength λ, (2) the increase in the lens numerical aperture (NA), and (3) the reduction of the process-related k1-factor, that are summarized in the Rayleigh equation: CD=k1⁢λNA Today, state of the art technology nodes rely on Extreme Ultraviolet Lithography (EUVL). The transition to shorter source wavelengths has entailed a continuous downscaling of the resist film thickness, e.g. to limit aspect ratios (i.e., resist height-to-width) in resist patterns when progressing to smaller feature sizes. High aspect ratios may otherwise lead to pattern collapse e.g. during the rinse process. The further development of EUVL involves transitioning from 0.33 NA to 0.55 NA, i.e. high-NA EUVL. A higher NA allows capturing higher diffractive orders which allows printing of even smaller feature sizes. With the transition to high-NA EUVL, again a reduction in resist film thickness (FT) is expected to avoid large aspect ratios. However, an additional reason is also given by a second Rayleigh equation which shows that the depth-of-focus (DoF) scales inversely with the quadratic of NA: D⁢o⁢F=k2⁢λNA2 The DoF gives the focus range that can be tolerated during the exposure. As a consequence, a reduced DoF will limit the range (and thus also the FT) over which the exposure contrast is high enough to deliver a good resist patterning performance. SUMMARY As realized by the inventors, the continuing reduction in resist FT will bring along associated challenges related to metrology. In particular, it is envisaged that a reduced FT will reduce the signal-to-noise ratio (SNR) of images obtained using e-beam based metrology, such as Critical Dimension Scanning Electron Microscopes (CDSEM). A lower SNR may in turn hinder accurate estimation of pattern roughness parameters, e.g. line-edge roughness (LER) or linewidth roughness (LWR), which are indicative of resist pattern performance. In light of the above, the disclosure includes a method of performing metrology on a microfabrication pattern, enabling accurate metrology on patterns in thin FT resists, and, more generally, on microfabrication patterns yielding low SNR. According to a first aspect there is provided a method of performing metrology on a microfabrication pattern, the method comprising: generating, by a scanning electron microscope (SEM), a first set of frames of a first region of the pattern, a second set of frames of a second region of the pattern, and a third set of frames of a third region of the pattern, wherein a number of frames of the first, second, and third sets of frames are different;for each of the first, second, and third set of frames, by a computing device:estimating feature data representing edge positions, linewidths, or centerline positions of one or more features of the region of the pattern of the set of frames (i.e. the region of the pattern represented/depicted by the set of frames), andcomputing a preliminary estimate of a roughness parameter from the feature data, wherein the roughness parameter is indicative of a line edge roughness, a linewidth roughness, or a pattern placement roughness of the one or more features;wherein the method further comprises, by the computing device:fitting a model equation to the preliminary estimates of the roughness parameter and a model parameter dependent on the number of frames of the set of frames, the model equation relating the model parameter to the roughness parameter; andcomputing a final estimate of the roughness parameter as an asymptotic value of the fitted model equation. According to a second aspect there is provided a non-transitory computer-readable media comprising instructions configured to, when executed by a computing device, cause the computing device to perform a method comprising, for each of a first, second, and third set of frames of a first, second, and third region of a microfabrication pattern, respectively, wherein the first, second, and third set of frames are generated by a scanning electron microscope (SEM), and wherein a number of frames of the first, second, and third sets of frame