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In-depth overlay contribution analysis of a poly-layer reticle F. Laskea, J. Whitteya, K.-D. Roetha, J. McCormacka, D. Adama, J. Bendera C. N. Berglundb, M. Takacb, Seurien Chouc a KLA Tencor MIE GmbH, Kubacher Weg 4, 35781 Weilburg, Germany b Northwest Technology Group Consulting, Inc., 16505 A SE 1st Street, Vancouver, WA 98684, USA c Synopsis, Inc., 700E Middlefield Road, Mountain View, CA 94043, USA ABSTRACT Wafer overlay is one of the key challenges for lithography in semiconductor device manufacturing, this becomes increasingly challenging following the shrinking of the device node. Some of Low k1 techniques, such as Double Exposure add additional burden to the overlay margin because on most critical layers the pattern is created based on exposures of 2 critical masks. Besides impact on overlay performance, any displacement between those two exposures leads to a significant impact on space CD uniformity performance as well. Mask registration is considered a major contributor to within-field wafer overlay.

We investigated in-die registration performance on a critical poly-layer reticle in-depth, applying adaptive metrology rules, We used Thin-Plate-Splinefit (TPS) and Fourier analysis techniques for data analysis. Several systematic error components were observed, demonstrating the value of higher sampling to control mask registration performance

Keywords: Mask metrology, registration, sampling, overlay, yield

Introduction In collaboration with a leading edge captive mask manufacturer, pattern placement performance of a poly layer reticle was measured using different sampling strategies to ascertain whether current methodologies for measuring pattern placement errors are able to find all important placement errors that can occur on a mask. An adaptive metrology technique was employed based on arrays with different pitches that captures successively small and smaller areas depending on the errors detected in larger area sampling plans. Measurements were performed using KLA-Tencor’s LMS IPRO4 on actual in-die features at thousands of locations across the reticle.

Typically photomasks today are dispositioned using a 3 sigma or maximum deviation value for X and Y placement errors based on an approximate range of thirty to three hundred measurement points. Usually these measurement points are registration targets located in the scribe of the exposure field on the reticle.

Depending on writing strategies, local charging, pattern densities, stripe field boundaries, plate flatness, noise, chucking and other effects, the measured features in the scribe are often times not representative of the errors within the exposure field itself 1 . The simplified error budget model shown in figure 1 illustrates some of the issues involved, where we have divided the error sources into three categories: random errors, spatially systematic errors that are slowly varying across the reticle (low spatial frequency), and spatially systematic errors that are short range in nature (high spatial frequency).

Starting at the 32nm node more detailed registration evaluations should be performed to ensure minimum impact from the reticle to wafer overlay yield.

This discrepancy between reported errors and true errors may lead to yield loss in manufacturing depending on the particular layer combinations6. Detected errors on the photomask measured for this paper were a combination of random and systematic errors. Given the magnitude of the systematic errors the question arises as to whether today’s sampling strategies and the employment of Gaussian statistics are the correct way to disposition product reticles. The goal of pattern placement metrology is to accurately estimate the nature and magnitude of placement errors based on a limited but adequate sampling strategy by applying the correct statistical models to the resulting population of data.

Metrology, Inspection, and Process Control for Microlithography XXIV, edited by Christopher J. Raymond, Proc. of SPIE Vol. 7638, 76382E · © 2010 SPIE · CCC code: 0277-786X/10/$18 · doi: 10.1117/12.848343

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Figure 1 – Simplified pattern placement error budget model5

METHODOLOGY All measurements were performed on an LMS IPRO4 pattern placement metrology system2. Performance specifications are shown in table 1. The LMS IPRO4 is capable of measuring in-die features such as contacts as small as 300 nm in size3. The particular layer chosen for measurements was a 45 nm node production poly layer due to its critical effect on device yields and performance.

Measurement

Specification

Nominal Accuracy

2.2 nm

Long Term Positional Repeatability

1.9 nm

Short Term Positional Repeatability

1.3 nm

Table 1 - Measurement performance specification of the LMS IPRO4 metrology system The measurement features were selected using Synopsys’s CATS marking program. The data was then output in a proprietary “MF3” format for input into the LMS IPRO4 measurement system.

Adaptive metrology principles were implemented4 by first creating a large number of registration measurements on a square grid (0.25 centimeter grid spacing) covering the entire reticle area on any measureable feature found at or near the grid sites. In this way measurements are made on a random selection of device features within the pattern, not just on test structures designed specifically for registration measurements (see figure 2).

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Figure 2 – Sampling plan to check for different systematic error components within total registration error Arrays of measurement points on a regular square (or rectangular) grid were chosen because of the Fourier and other data analysis tools available. Given the set of measurement data on the 0.25 centimeter spacing array, the relative translation, rotation, and isotropic scale errors are minimized using first order regression. The next step is to carry out a thin plate spline regression analysis on the data in order to separate out and quantify the systematic errors that are slowly varying across the reticle such as might arise from stage calibration residuals. After subtracting these errors from the original data, the remaining errors are analyzed to identify, separate out, and subtract as many of the systematic errors as possible. Those errors that remain after removing the systematic errors are then assumed to be dominated by errors that can be treated as random, although they may also include systematic errors with spatial signatures that have not yet been identified.

SYSTEMATIC PATTERN PLACEMENT ERROR COMPONENTS

Figure 3 – Typical systematic pattern placement error components: low spatial frequency, high spatial frequency and pattern dependent

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As mentioned above, the final mask registration quality is a sum of errors caused by several different factors. Each factor drives specific error signatures like low spatial frequency, high frequency or pattern dependent (see figure 3). While slowly varying signatures are covered well with today's advanced mask users’ measurement strategies of 200 and more sample points, local image displacement hotspots remain invisible. These hotspots occur at specific locations, where all registration errors merge into a severe image displacement. This local registration error can cause electrical issues at the final chip and thus is a manufacturing risk for mask users. To better understand that risk and to develop an appropriate sampling plan, the following pages will explain the nature of these signatures in more detail.

It is expected that all reticles will exhibit some registration errors that are slowly varying spatially across the reticle.

While it is possible that some slowly-varying spatial systematic error contributors will be pattern dependent, it is more likely that the largest ones will be pattern independent and several may have commonality from reticle to reticle (for example, pattern generator stage calibration errors will be common between calibrations). Such errors can arise from stage calibration and operational errors within pattern generators, from gravitational, clamping, thermal, and other forces on the reticle plate during printing, and from reticle processing after printing. While such registration errors are not likely to exhibit Gaussian statistical characteristics, they are in principle relatively easy to measure and characterize using a coarse array of measurement points covering the entire reticle area. If the spatial characteristics of the errors are known, then measurement points that adequately sample all relevant areas of the reticle can be chosen for measurement that will provide representative results. Generally, slowly occurring spatial errors that are stable and that occur on various product types can be corrected by the e-beam lithography system. However if the spatial characteristics are not known or vary from reticle to reticle, then it is necessary to measure a sufficiently large number of points over the entire reticle area in order to obtain representative results and track down the sources of the error. The major concern with such errors, as is the case here, is that the errors may be quite non-uniform across the plate such that the maximum errors are localized. In such a case calculations of the 3Ďƒ value of the errors will not necessarily reflect the registration characteristics of importance to the reticle user. With a small number of measurement locations, the results will be strongly influenced by measurement point locations and the spatial distribution of such errors across the reticle. Figure 4 shows the analysis of the slowly varying spatial errors using a Thin Plate Spline Function.

Figure 4 – Raw registration map (left); derived slowly varying spatial error contributions (middle) using a Thin Plate Spline function; and the residual errors after 1st order corrections (right)

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One might conclude from this figure that only random errors remain within the residuals. However spatially-dependent error sources are present and some of these are exposed when the error data is put through a Fourier filter. In this powerful analysis technique the error distribution is first transformed to the frequency domain using a fast Fourier transform, then two parts of the spectrum are reverse transformed separately. One reverse transformation includes only the corresponding frequency components along the u- and v-axis, and the other includes all other components. In this way any error sources that have either x- or y-orientation or periodicities are included in the former, and the remainder is included in the latter. Since many potential systematic error sources such as some pattern dependencies and pattern generator errors have x-and/or y-periodicities, this scheme allows these to be separated from random and other systematic errors. Fourier-filtered error distributions will be referred to as axis-oriented error distributions. The results of this data filtering was based on two 51 by 51 arrays of measurement points with grid spacing of 100 µm. The locations of the arrays were chosen after examination of the reticle pattern layout and the results of measurements at the larger grid spacing. These locations are shown in Fig. 5a along with the location of the smaller 6 µm grid spacing array discussed later in this report. Note that both sets encompass a vertical (y-direction) data-file map boundary. The registration error data for one of these arrays (Array 1-100µm) after correction for translation, rotation, and isotropic scale is shown in Fig. 5b. The data-file map boundary is along the center “dense” vertical column. Because of the sparse measurement data on the left side of this array (shown cross-hatched), however, it was decided to restrict further analysis on this data set to the right side of the array. After first order correction for translation, rotation, and isotropic scale of the right side of the data set followed by Fourier filtering, the axis-oriented registration error distribution is shown in Fig.6.

One striking feature of the registration error distributions shown in Fig. 6 is the preponderance of positive x-direction registration errors with an average offset value of 6.2 nm on the data column located near or at the data-file map boundary. These errors appear to be localized on this column and do not extend to the next column that is 100 µm away.

a. Location of smaller Grid Pitch arrays overlaid on 0.25 cm array error map

b. Reg. error distribution from array 1 – 100 um.

Figure 5 – Smaller size measurement arrays

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a. Error magnitudes and wavelengths b. line view of center row image placement waveform

c. Reg. error dist.

From array of 1 -100 um.

Figure 6 – FFT analysis center column in array 1 measurements showing error frequencies at ~250µm and ~500µm spatial wavelengths When such error sources exist they appear as peaks in a Fourier transform of the error distribution. However the other peaks in both x- and y- shown in the figure 6a with 250µm and 500µm periodicities are also found in the Fourier power spectrum of the Array 2-100µm error distribution, and they do represent periodic errors. These errors as well as the center column x-direction offset in Array 1-100µm are illustrated by analyzing only the center column of Fig. 6b. The zoom in this figure is a reverse transform of only the larger Fourier components to produce the systematic spatial image placement line view reflecting only these spectral peaks. This line view exposes both the x-direction offset at the datafile map boundary of Array 1-100µm and the 250µm and 500µm error periodicities apparent everywhere in the error distributions of both Array 1-100µm and Array 2-100µm. The fact that the 250µm and 500µm periodic errors are found at approximately the same magnitudes in x and y on both arrays suggests that the error mechanisms responsible for these peaks affect all regions of the reticle, and that they are likely caused by pattern independent sources. Based on experience with pattern generators it is likely that these errors are related to distortions across the printing stripe of the pattern generator.

Next, a strong correlation between registration error direction and nearby pattern density asymmetry was found as illustrated in table 2. The measurement points with asymmetric adjacent feature density all show a significant mean registration displacement toward the region of densest pattern surrounding each measurement point, although the magnitude of the displacement averages 1 nm or less. This result verifies that there are one or more registration error mechanisms that are dependent on short-range pattern density asymmetry near the measurement point. The direction of the effect, moving features toward the areas of densest pattern, is consistent with electron exposure proximity effects but it is recognized that it might also be caused by other mechanisms having asymmetric pattern density dependence.

0.25 cm Array

0.14 cm Array

Measurement Point Type

Mean x-error

Mean y-error

Mean x-error

Mean y-error

Points with uniform adjacent features Points with dense features toward – x Points with dense features toward + x Points with dense features toward – y Points with dense features toward + y

-0.06 nm -1.63 nm 1.03 nm -0.53 nm 0.25 nm

0.01 nm 0.36 nm 0.53 nm -0.85 nm 1.1 nm

-0.02 nm -0.67 nm 0.47 nm -0.25 nm 0.03 nm

-0.03 nm 0.24 nm 0.07 nm -0.7 nm 0.45 nm

Table 2 - Registration Error Dependence on Pattern Density Asymmetry

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Figure 7 - Illustration of the five proximity types near measurement points In order to further investigate the short range placement errors, an array with 6µm spacing was investigated. One set of measurements with 6µm grid spacing was made, and both the grid spacing and the location of the array were chosen to maximize the number of points that could be measured as well as provide some pattern variation to expose patternsensitive errors. Because of the small grid spacing this array is only sensitive to systematic or pattern dependent errors that have very short range, and was examined with the objective of finding and characterizing both short range patternindependent systematic errors and short-range pattern sensitive errors. The total 3σ error for this array is below 4 nm for both x and y, so short-range errors of all kinds do not contribute strongly to the overall registration errors measured on this reticle.

The results from the 6µm grid spacing measurement exposed some additional systematic errors that may be pattern independent. These errors have periodicities less than 20µm and are unlikely to be an exact sub-multiple of any chosen sampling strategy. Since only one such array was measured in this study, it is possible that there may be other significant very-short-range pattern-independent error sources that are present elsewhere on the reticle but not in the array measured here. It is unlikely that the origin of most such errors will be from the pattern generator.

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SAMPLING IMPACT TO YIELD RISK There are two risks of mis-qualification errors that can occur as part of a quality control check. These two types of errors are called Alpha and Beta errors. An Alpha error (or Type 1 error) is the probability that an “in-control” mask will be rejected and a Beta error (type II error) is the probability that an “out-of-control” reticle will be shipped to the wafer manufacturer.

For the following evaluation the measurement data of one mask were used and fractured in groups for evaluation of the deviation from the intended position. Figure 10 represents the relationship between 3 sigma results and sample size. In an effort to illustrate the relationship between 3 sigma measurement values and sample size the extended measurement job was used to simulate various sampling sizes. Sub-samples of measurement sites with a given number of measurement targets were selected randomly from the extended measurement job by statistics software. Then the mask performance was calculated based on the reduced sampling size and the results shown in fig.10. The boxes in the plot represent 50% of the sample population values, the bar extension represent the 3 sigma value and the dots can be interpreted as outliers. The line in the middle of each box is the median value of the sample population.

The chart demonstrates the stability of 3 sigma values in relationship to the sample population size. By increasing the sampling size the risk of alpha or beta errors is reduced. Let us assume first that the reticle was manufactured to meet a 9.5nm registration spec (green dotted line in Fig.10). There is a fairly high chance that this reticle would be accepted and shipped to the wafer fab if the metrology sampling is below 400. The chance to achieve a measurement result at or below 9.5nm is relatively high especially with small sample sizes.

If, on the other hand, the reticle would be manufactured to meet 11nm registration spec (red dotted line in Fig.10), then, using a small sampling size below 400, there is a risk that the mask would be rejected just because the sampling of the registration metrology targets would lead to a metrology result exceeding the reticle spec.

Smaller alpha & beta yield risk

Product measurement example

Assumed product specification: 11nm Assumed product specification: 9.5nm

Increased measurement effort

Figure 10. Evaluation of the data of one mask measured demonstrates yield risk dependent on registration sampling size, the dotted lines show assumed product specifications of 9.5nm (green) and 11nm (red), respectively. 50% of the data are in the boxes, and 50 % outside.

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SUMMARY The evaluation of the registration error of one poly layer reticle shows that the error is dependent on the metrology sampling strategy. Measuring only few points, the results will be strongly influenced by the choice of measurement point locations relative to the spatial distribution of any systematic error across the reticle. It was derived, that Alpha and Beta risk of final mask quality control are increasing significantly with lower sampling sizes below 500 measurement targets.

The magnitude of registration errors on a reticle resulting from the combination of different error mechanisms impacting it during fabrication is likely to vary not only from mask shop to mask shop, but also from reticle to reticle within a given mask shop. For example the error contributors from pattern generators are expected to vary from machine to machine, and they will also vary with time and with the way in which the tools are set up and used for mask making.

Furthermore any pattern-specific errors sources will affect different reticle patterns in different ways.

The data from the single reticle measured in this study demonstrate the importance of sampling strategy in exposing many of these fundamental registration error mechanisms and their characteristics. Since the contribution of these different registration error mechanisms will vary from mask to mask, it is important that any sampling plan intended to quantify registration errors take into account the characteristics of the different error mechanisms that might exist.

Registration Sampling Challenge

Registration result stability

(30-300 sampling points)

Increased product sampling with adaptive metrology (>>500 sampling points)

Partially described

Well described

(incl. frequency dependent components)

No information

Sensitive

Pattern dependent

No information

Spot tests

Alpha&Beta yield risk

High

Reduced

Slowly spatial Qualification of reticle image placement errors

Current product sampling

Rapidly spatial

Table 3 - New strategy of registration metrology sampling is required in order to reduce wafer yield risk The objective of reticle acceptance testing for registration is to obtain a reliable and representative measurement of the registration error that is likely to exist on any feature within a given reticle. Then the degree of accuracy (or confidence level in the results) is depending on • the total number of points measured on the plate according to the chosen sampling plan; • the strategy used for choosing the measurement points in the sampling plan and the effectiveness of matching these measurement points to the actual registration error sources on the reticle; and • the effectiveness of the data analysis scheme used to interpret the data.

There is a tradeoff that can be made by the mask user between the number and location of points measured to determine registration error characteristics on a reticle and the confidence level or degree of uncertainty acceptable for the results.

Such a tradeoff is best made given a full understanding of the characteristics of all potential registration error contributors. However many users depend on registration error measurement strategies and data analysis/interpretation schemes that are not closely tied to the real registration error sources or their spatial characteristics. Therefore registration quality check sampling plans should be reviewed in order to lower mask users’ risk of having yield and performance issues caused by invisible mask registration errors.

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References [1] [2] [3] [4] [5] [6]

Weidung Wang: “Adaptive Metrology and Mask Inspection,” Stanford Solid State Electronics Laboratory, Semiconductor Research Corporation Contract No. MC-515, December 1997.

Roeth, K.-D., Antesberger, G., Enkrich, C., Laske, F., Adam, D., “Registration Metrology for 45nm Node Reticles”, SEMI Technology Seminar 2007, Makuhari, Japan.

Enkrich, C., Antesberger, G., Loeffler, O., Roeth, K.-D., Laske, F., Adam, D., “Registration Measurement Capability of VISTEC LMS IPRO4 with Focus on Small Features”, Photomask Japan 2008, Yokohama, Japan.

Schultz, B., Seltmann R., Busch, J., Hempel, F., Cotte, E., Alles, B., “Meeting Overlay Requirements for Future Technology Nodes With in-die Overlay Measurements”, Advanced Lithgraphy 2007, Santa Clara, CA Cotte, E., Alles B., Wandel T., Antesberger, G., Teuber, S., Vorwerk, M., Frangen, A., Katzwinkel, F., “193-nm Immersion Photomask Image Placement in Exposure Tools”, Optical Microlithography XIX 2006, Vol. 6154 G.Hughes: Mask Metrology – Current and Future Challenges, http://www.eeel.nist.gov/812/conference/2009_presentations/Hughes.pdf, 2009

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