Evaluation of producer’s and consumer’s risks in scatterometry and scanning electron microscopy metrology for inline critical dimension metrology

Asano, Masafumi; Ikeda, Takahiro; Koike, Toru; Abe, Hikaru

doi:10.1117/1.2397034

Cited by 10 publications

(4 citation statements)

References 13 publications

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“…As pointed out by Asano et al it is risky to build the analysis solely on scatterometry and one should also include a sampling plan with e.g. SEM analysis [138]. Here the tool-totool matching is important for lowering the combined uncertainties [139].…”

Section: Discussionmentioning

confidence: 99%

Scatterometry—fast and robust measurements of nano-textured surfaces

Madsen

Hansen

2016

Surf. Topogr.: Metrol. Prop.

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Scatterometry is a fast, precise and low cost way to determine the mean pitch and dimensional parameters of periodic structures with lateral resolution of a few nanometer. It is robust enough for inline process control and precise and accurate enough for metrology measurements. Furthermore, scatterometry is a non-destructive technique capable of measuring buried structures, for example a grating covered by a thick oxide layer. As scatterometry is a non-imaging technique, mathematical modeling is needed to retrieve structural parameters that describe a surface. In this review, the three main steps of scatterometry are discussed: the data acquisition, the simulation of diffraction efficiencies and the comparison of data and simulations. First, the intensity of the diffracted light is measured with a scatterometer as a function of incoming angle, diffraction angle and/or wavelength. We discuss the evolution of the scatterometers from the earliest angular scatterometers to the new imaging scatterometers. The basic principle of measuring diffraction efficiencies in scatterometry has remained the same since the beginning, but the instrumental improvements have made scatterometry a state-of-the-art solution for fast and accurate measurements of nano-textured surfaces. The improvements include extending the wavelength range from the visible to the extreme ultra-violet range, development of Fourier optics to measure all diffraction orders simultaneously, and an imaging scatterometer to measure area of interests smaller than the spot size. Secondly, computer simulations of the diffraction efficiencies are discussed with emphasis on the rigorous coupled-wave analysis (RCWA) method. RCWA has, since the mid-1990s, been the preferred method for grating simulations due to the speed of the algorithms. In the beginning the RCWA method suffered from a very slow convergence rate, and we discuss the historical improvements to overcome this challenge, e.g. by the introduction of Li's factorization rules and the introduction of the normal vector method. The third step is the comparison, where the simulated diffraction efficiencies are compared to the experimental data using an inverse modeling approach. We discuss both a direct optimization scheme using a differential evolution algorithm and a library search strategy where diffraction efficiences of expected structures are collected in a database. For metrology measurements two methods are described for estimating the uncertainty of the fitting parameters. The first method is based on estimating the confidence limits using constant chi square boundaries, which can easily be computed when using the library search strategy. The other method is based on calculating the covariances of all the free parameters using a least square optimization. Scatterometry is already utilized in the semiconductor industry for in-line characterization. However, it also has a large potential for other industrial sectors, including sectors making use of injection molding or roll-2-roll fabrication. Using the...

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Section: Discussionmentioning

confidence: 99%

Scatterometry—fast and robust measurements of nano-textured surfaces

Madsen

Hansen

2016

Surf. Topogr.: Metrol. Prop.

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“…18) The risks for overlay can also be evaluated using the OC curve, which is a plot of the probability of acceptance LðpÞ and the fraction of defectives p. Figure 5 shows an example of an OC curve. If all samples are measured, the curve becomes a step function, as shown in the ideal sampling in Fig.…”

Section: Proposal Of New Acceptance Variablesmentioning

confidence: 99%

“…It was shown that the operating characteristic (OC) curve clarifies the producer's risk and the consumer's risk arising from lot acceptance sampling for CD. 18) The risks for overlay can also be evaluated using the OC curve, which is a plot of the probability of acceptance LðpÞ and the fraction of defectives p. Figure 5 shows an example of an OC curve. If all samples are measured, the curve becomes a step function, as shown in the ideal sampling in Fig.…”

Section: Proposal Of New Acceptance Variablesmentioning

confidence: 99%

Skewness and Kurtosis Risks of Quality Control in Overlay Inspection

Sato¹,

Ikeda²,

Kasa³

et al. 2010

Jpn. J. Appl. Phys.

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The population of overlay errors in layer-to-layer structures in semiconductor manufacturing often obeys non-normal distribution functions such as elliptic, rectangular, and skewed distributions. Thus, there can be many wrong diagnoses of quality control that can lead to consumer's risk and producer's risk. In a lot acceptance test, a product should be judged by variables rather than by attributes because of the small sample size. A new acceptance test by variables was introduced for a non-normal population. It included sample skewness and kurtosis as well as mean and standard deviations. Using operating characteristic (OC) curves, it was shown that sampling inspection by variables can be applied safely to a strong nonnormal population with new variables. #

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“…The advantages and disadvantages of these two CD metrologies have been well discussed [3,5]. The target of CD uniformity (CDU) for advanced technology has been driven to 1 nm, i.e.…”

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confidence: 99%

Sub-nanometer pitch calibration and data quality evaluation methodology

Wang

Huang

et al. 2008

SPIE Proceedings

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Average CD of CD SEM and scatterometry CD (OCD) have been adopted for advanced CD control. The advantages and disadvantages of these two CD metrologies have been well discussed [3,5]. The target of CD uniformity (CDU) for advanced technology has been driven to 1 nm, i.e. about three and half the size of a silicon atom, which is 0.29 nm. In the real production environment, engineers need to face sub-nanometer (< 1 nm) CD variations and do the necessary process corrections to meet the 1-nm CDU requirement. In other words, advanced CD process control has already been in the world of atomic scale. It turns out that methodology to ensure the accuracy of sub-nanometer CD has become essential for advanced CD control.In this paper, we introduced a methodology to produce 0.25, 0.5, and 1 nm programmed pitch offsets through mask design. These offsets are attainable with current process capability. Pitch offsets instead of line/space width offsets were used because the pitch is relatively process insensitive. The pitch has already been widely used as a CD SEM magnification calibration standard, e.g. Hitachi m-scale 240-nm pitch and VLSI 100-nm pitch standards. We produced large and small pitch splits to meet different magnification linearity requirements. We also used optical CD to verify the programmed pitch offset. Using the raw spectrum of OCD, systematic pitch signal changes in 0.25-nm steps can be detected, ensuring that 0.25-nm pitch offset standards are meaningful. Interestingly, 0.25 nm is smaller than the 0.29-nm Si atom.We also used this standard wafer to do the sampling size or data quality evaluation for different CD SEM measurement methodologies, e.g. 150K by 150K or 80K by 35K magnifications that in turn dictates the sample size. Pitch sensitivity is strongly related to the sampling size and line-edge roughness (LER). For example, 0.25-nm pitch sensitivity needs a larger sampling size than those of 0.5-nm and 1-nm pitch sensitivities.By means of this standard wafer, we can easily quantify metrology quality as well as choose the right metrology and sampling size for advanced process control.

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Evaluation of producer’s and consumer’s risks in scatterometry and scanning electron microscopy metrology for inline critical dimension metrology

Cited by 10 publications

References 13 publications

Scatterometry—fast and robust measurements of nano-textured surfaces

Scatterometry—fast and robust measurements of nano-textured surfaces

Skewness and Kurtosis Risks of Quality Control in Overlay Inspection

Sub-nanometer pitch calibration and data quality evaluation methodology

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