Magazine winter05 gating factor higher yield

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The Gating Factor to Higher Yield Application of Scatterometry for Inline Prediction of Electrical Performance Matthew Sendelbach, Chas Archie, Bill Banke, IBM Microelectronics Jason Mayer, Formerly of IBM Microelectronics Hideaki Nii, Toshiba America Electronic Components Pedro Herrera, Matt Hankinson, KLA-Tencor

Currently, CD-SEMs are the tool of choice for inline gate length measurements for most semiconductor manufacturers. This is mainly due to their flexibility, throughput, and ability to correlate well to physical measurements (e.g., XSEM).

Scatterometry, however, is still being used by an increasing number of manufacturers to monitor and control gate lengths. But can a scatterometer measure such small critical dimensions well enough? In this article, we explore this question by analyzing data taken from wafers processed using 90 nm node technology. We also show how total measurement uncertainty (TMU) analysis is used to improve the scatterometry model and understand the relative contributions from obstacles that hinder even better correlations.

Introduction

A critical component of device performance is the effective control of gate linewidth in manufacturing. There are several critical dimension (CD) metrology technologies currently used, including CD-SEM, electrical CD, CD-AFM, and scatterometry. The ideal measurement should provide the linewidth information and throughput required for an inline measurement, while providing adequate correlation to electrical performance. In this article, we will address several key questions essential for the adoption of scatterometry CD technology for high-volume manufacturing at the 90-nm node and beyond. Does the technique provide a measurement of sufficient quality for line control, and how should that quality be quantified? Can the measurement provided by scatterometry CD on grating targets be directly related to critical electrical measurements (Lpoly, for example)? What is the best grating pitch for scatterometry to monitor? Previous authors have compared multiple CD metrology techniques. For instance, Kye and Levinson1

investigated methods for achieving good correlation between electrical CD and CD-SEM to reduce the offset.

Correlation between scatterometry CD and electrical parameters (i.e., drive current) was established by Hodges et. al.2 Solecky, Mayer, and Archie3 have investigated methods to improve the correlation between CD-SEM and electrical test, and discuss the appropriateness of metrics to evaluate correlation between metrology systems. These evaluation metrics were formalized by Sendelbach and Archie4 with a rigorous statistical analysis, called total measurement uncertainty (TMU) analysis, to compare multiple metrology techniques. This article presents recent work to provide a statistically rigorous comparison (using TMU analysis) between scatterometry and electrical data to establish scatterometry as a viable measurement technique for inline prediction of electrical performance.

Wafer samples and their measurement

Wafer description IBM fully integrated 200 mm SOI product wafers at the 90 nm node were used in this experiment. After gate formation, the wafers were measured on both a CD-SEM and a scatterometer. The CD-SEM is a Winter 2005

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respected model, but one to two generations behind the current generation. Although several scatterometers are available in the market, the KLA-Tencor SpectraCD optical CD metrology tool was chosen for this work. At this process step, the film stack consisted of 150 nm of a polysilicon/gate oxide/SOI substrate (the top portion of which was n or p doped, depending on the structure).

The four wafers used for this experiment were processed with focus-exposure matrices (FEMs), so that correlations could be established over a sufficiently large range of CD. After gate formation, the wafers were further processed until they reached the electrical Lpoly measurement at the first metal level (M1) in the back-end-of-line (BEOL).

Measurement sample plan and the CD-SEM measurement Eight chips per wafer were measured, each having a different dose/focus combination. Seven different structures were measured on each chip. Four of them were 50 µm x 50 µm n-doped scatterometry targets with alternating lines and spaces of varying pitch (245, 402, 507, and 595 nm), while two structures were Lpoly electrical macros (one n-doped and one p-doped) with a 770 nm pitch. The seventh structure was a location commonly used for lot dispositioning for CD measurements. This will be referred to as the disposition target, or “dispo,” target. Table 1 shows the metrology methods used to measure each structure. The scatterometer and electrical tester only made one measurement per structure; the CD-SEM, however, made four measurements per structure in a 2 x 2 array in order to better capture across-structure CD variation. Each of the four CD-SEM measurements for a given structure will be referred to as a repetition, or “rep,” throughout the paper. Each rep has its own (x, y) chip coordinates for a given structure type. For example, “rep 2” measurements for the 245 nm scatterometry target are always located at the same coordinates (x2, y2) within the chip, regardless of the chip or wafer.

Measurement Structure 245 nm pitch scatterometry target 402 nm pitch scatterometry target 507 nm pitch scatterometry target 595 nm pitch scatterometry target 770 nm pitch nLpoly electrical macro 770 nm pitch pLpoly electrical macro disposition target

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Metrology System Scatterometry CD-SEM 1 4 1 4 1 4 1 4 4 4 4

The CD-SEM measurement conditions and algorithm were standard for measuring polysilicon line structures in manufacturing. The algorithm had previously been optimized by TMU analysis for both nested and isolated lines.

The Lpoly measurement The Lpoly measurement is derived from electrical capacitive measurements by measuring two different gate capacitor structures. The first is a fin-type capacitor which consists of multiple, identical, parallel gate electrode structures; these are connected at the ends to form a single capacitor. The measured capacitance of this structure, Cmeasured, is related to the capacitance of a single gate electrode, Celectrode, by Cmeasured = NCelectrode ,

where N is the number of electrodes. Celectrode includes the capacitive contribution from the gate sidewalls. The other structure is a large rectangular plate capacitor. The capacitance per unit area of this structure is defined as cgate. Due to the large length and width of this capacitor, the contribution due to the gate sidewalls is negligible.

The total capacitance of a single gate electrode, not including the sidewalls, can then be represented by Cgate = WLpoly cgate = Celectrode –2Cfringe,

Lpoly

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Cmeasured –2Cfringe = N Wc gate

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Now that the measurements have been described, it is important to understand how the measurement data were analyzed in order to draw conclusions. An effective method to optimize Electrical the scatterometry model, as well as draw meaningful conclusions from the data, is accomplished through TMU analysis.

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where W is the device width, Lpoly is the electrically measured gate length, and Cfringe is called the fringe capacitance. Cfringe represents that portion of the capacitance contributed by one side of the gate sidewall, and is calculated using device simulation. Lpoly can then be isolated:

Table 1. Metrology systems used to measure the structures. Four CD-SEM measurements was made per structure per chip.

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Total measurement uncertainty (TMU)

Using TMU analysis TMU analysis can be used to optimize a metrology system, assess a system, or compare different measurement methodologies. This

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article explores all three uses. Fundamentally, TMU analysis is a calibration exercise using artifacts relevant to the application and comparison of a tool under test (TuT) with a reference measurement system (RMS). Details of the Mandel linear regression, where both variables are subject to error, have been previously published.4 This work uses TMU analysis for assessing the correlation of two methodologies. In such a situation, there may not seem to be a clear RMS. However, when the goal of the correlation study is understood, the data set to be associated with the RMS becomes clear. As an example, consider a CD-SEM measurement on the Lpoly structure versus a CD-SEM measurement on a scatterometry grating. The question being posed is, “Does the use of a different target from the structure that is ultimately used to assess the manufacturing process by an electrical measurement introduce loss of correlation?”

Given the nature of the question, it seems appropriate to identify the CD-SEM measurement on the Lpoly structure as the RMS. This has the implication that any nonlinearity in the correlation is associated with the CD-SEM measurement on the scatterometry grating, i.e., the TMU of this latter methodology should contain this nonlinearity.

Uncertainty of TMU‡ As with any measurement, and especially statistical metrics, the results are actually estimates. Generally speaking, the uncertainty of the estimate decreases as the number of measurements that go into determining the estimate increases.

TMU is derived from the difference of two variances: TMU2 = 9(σˆ 2Mandel – σˆ 2RMS)

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where σˆ 2Mandel is the Mandel output variance, i.e., the square of the Mandel net residual error, and σˆ 2RMS is the measurement uncertainty variance of the RMS. σˆ 2Mandel is an output of the Mandel linear regression.

This variance is a measure of the scatter of the data about the best-fit line, while TMU is a measure of how much of this scatter can be attributed to the TuT. σˆ 2RMS must be determined in a separate exercise from the TMU calculation.

When σˆ 2RMS is well determined with negligible uncertainty, the uncertainty of TMU is governed by the uncertainty of σˆ 2Mandel. Now, if the study is repeated, we will see slightly different data, because each measurement includes a random component which contributes to

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measurement precision. Assuming that the residual errors of the Mandel regression follow a normal distribution, then the ratio of Mandel output variance estimate to the full population variance follows a chi-squared distribution which depends only on the number of data pairs. A confidence interval for σˆ 2Mandel can then be determined based upon the chi-squared distribution. TMU uncertainty bounds presented in this article used this methodology with a confidence level of 90 percent.

Development of the scatterometry model

Overview of model development Now that a method for assessing the success of a scatterometry model has been identified, we will begin our discussion of the model development process. Developing a scatterometry model involves using the film stack information (thickness and dispersion of each film) and the grating information (pitch, height, and profile characteristics) to create a theoretical model of the measured structure. Generally, one should use any reference information available to characterize the films and profiles to assure a “correct” scatterometry model.

This reference information can include XSEM or AFM results for understanding the profile and blanket wafer spectra for modeling the film properties. The wafers in this experiment were product wafers and, therefore, available for only a limited amount of time. This prevented collection of any of the reference data, and mandated the use of existing film dispersion files from similar engineering wafers and the reliance on previous process knowledge for developing the initial grating profile. Determining the final model—which was aided by the availability of CD reference data and use of TMU analysis—required choosing which features to include or exclude from the profile, which parameters to fix or vary in the model, and the value and range of each parameter. The process and steps used for moving from the initial model to the final model are discussed below.

Initial model development

The first step of model development was to create test libraries to study the general characteristics of the structure (grating on top of SOI stack). These libraries represented profiles ranging from the simplest to the most complex, and covered a large range of process variation. A library is a collection of theoretical signatures, where each signature represents a unique set of parameter values that describes the structure. By reviewing these theoretical signatures, one can understand how a change in a particular profile parameter affects the spectra. In addition Winter 2005

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The spectra show the same type of CD-related variation (primarily due to a change in dose) predicted by the theoretical models for wavelengths from 250 nm to 380 nm. A similar evaluation was then completed to understand the spectral variation associated with multiple profile parameters.

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Figure 3 shows the variation associated with a change in the amorphous (doped) silicon thickness and a change in the polysilicon thickness. As shown, a change in these two parameters produces very similar spectral variation. When two or more parameters have similar spectral signatures, they are “correlated” (e.g., a change in parameter A shows the same effect as a change in parameter B). In such a case it is advisable to fix one or more of the parameters, or choose a different model to eliminate or reduce the correlation.

to the profile parameter effect on spectra, one must also consider the effect of under-layer thickness variation.

For example, Figure 1 shows the spectral effects associated with a change in the CD and in the SOI thickness.

Wavelength (nm) Black: Lib, Red: Data Match Range: Blue

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If the theoretical model is accurate, then one should be able to detect similar behavior in the measured spectra.

Figure 2 shows three measured spectra from one product wafer where each spectrum is from a different focus/dose condition.

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Ultimately, one wants to choose a setup having sensitivity to the critical parameter(s) (CD in most cases) and little or no correlation between a critical parameter and another parameter (while not excluding other important parameters). In addition to meeting the sensitivity and correlation requirements, we wanted all the gratings to use the same model where the only major difference is the grating pitch. That is, all the models, were required to use the same set of fixed and varied parameters, even though there may be a different set of optimum parameters for the 245 nm structure versus the 595 nm structure. This requirement was imposed because models that are essentially independent of pitch are considered easier to implement in manufacturing, where a sudden change in the monitored pitch may be needed. The models used for this work met this requirement.

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After reviewing the initial libraries for each grating, we decided to exclude wavelengths larger than 380 nm from future models. Removing these wavelengths allowed the under-layers to be fixed and reduced the size of future models and the time required to generate them. Different sets of varied profile parameters were then applied to the measured spectra to judge the quality of fit and to characterize the measurement behavior (e.g., CD changes through exposure, range of CD variation, etc.). One of the basic steps for developing good scatterometry models is to review the results to be sure that the fit between measured and theoretical spectra is good and the results “make sense.” One can over-model or incorrectly model an application to achieve a good fit, but obtain unrealistic or incorrect results. By iterating through different sets of varied parameters across all four gratings, we determined that a model with four varied parameters (four Degrees Of Freedom, or DOF) could be used to build libraries for this application, where all of the varied parameters are located in the grating profile. The analysis up to this point was focused on determining what can be measured based on the theoretical and measured spectral variation. The next step was determining what needs to be measured in order to provide good CD correlation to the reference electrical measurements.

Improving the model

The four DOF model chosen based on the results of the theoretical and measured spectral analysis was then used to generate a library for each grating. These libraries were used to measure the spectra from each grating and compare them to the nLpoly and pLpoly reference measurements. By using TMU analysis it was possible to test the libraries and determine whether a four DOF profile provided good correlation on all four gratings, or if more or fewer degrees of freedom were necessary. Results of this process indicated that a simpler three DOF model provided better correlation to Lpoly measurements on all four gratings. Three DOF libraries were generated and used to provide the “by chip” and “by wafer” results necessary for the correlation analysis. Further changes to the model to improve TMU using the CD-SEM as the reference tool were explored but not implemented, because such improvements only slightly improved the correlation to the CD-SEM measurements, while decreasing correlation to the electrical measurements.

This was counter to the goal of maximizing the electrical measurement correlation. The final model varied the height of the profile, the width of the profile, and the sidewall angle of the polysilicon portion. Figure 4 shows a representative profile.

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Modeling and measurement techniques not explored As previously mentioned, the use of product wafers did not allow for the collection of 3D or blanket film reference data. In addition to mandating the use of existing film dispersion files, the limited time also prevented the evaluation of all SpectraCD modeling capabilities.

Future analysis of correlation to electrical measurements and CD-SEM measurements would benefit from the following additional studies.

Updated film dispersion files

A key step in developing the scatterometry models involved choosing a wavelength range where changes in the underlying films had no effect on the spectra. This allowed simpler (fewer DOF) models to be used and reduced the time needed to generate new libraries. However, changes in the dispersion properties may still affect the model’s goodness-of-fit and TMU behavior because films are also used in the profile. SpectraCD allows one to measure film thickness and dispersion properties on a wafer-to-wafer or site-to-site basis and feed these values into the scatterometry measurements. An evaluation of this capability using a larger number of wafers processed over a longer period of time would aid in determining whether accounting for these film differences improves TMU and/or is necessary for maintaining good TMU over time. At the very least, one could use blanket film regions from the product wafers to optimize the film dispersion files used for generating a library instead of simply relying on existing dispersion files.

Extended wavelength range

Spectra were collected from wavelengths ranging from 250 nm to 750 nm. Current generation scatterometry tools allow wavelengths to be collected down to 190 nm.

The use of these smaller wavelengths may provide increased sensitivity to profile features and improved scatterometry measurement performance and stability.

All films (grating and under-layer films) would need to be measured and modeled down to wavelengths of 190 nm for use in the scatterometry models in order Figure 4. Representative profile of to do this analysis. final model.

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Structure-to-structure profile differences

The use of four structures with pitches varying from 245 nm to 595 nm means that a large range of line:space ratios are being measured by the CD-SEM and the scatterometer with little change to each metrology tool’s measurement setup. Different profile characteristics (sloped and re-entrant sidewalls, corner rounding, etc.) caused by the different line:space densities should be evaluated to verify that both metrology tools are using an optimum measurement setup. Evaluating these profiles would help one understand how differences in those profiles are affecting TMU results and allow for further improvement to the correlation between inline metrology tools and electrical measurements.

Data analysis

“By chip” versus “by wafer” correlation Correlation results are presented in this article using two basic ways to organize the data. The first consists of plotting data from each individual chip separately.

This means that, with four wafers and eight chips per wafer, 32 data pairs are plotted on the correlation plot, except for a handful of cases where the CD-SEM unsuccessfully measured all four “reps” for a given structure within a single chip. These unsuccessful measurements were not detected during data collection because they occurred during fully automated overnight runs. Once the unsuccessful measurements were noticed, it was not worthwhile to go back and collect these data because of their small number, and because the wafers were product with a tight schedule. The correlation plots showing data from individual chips are referred to as “by chip” plots, and are useful for assessing how well measurements taken from a single chip compare to measurements collected using an RMS. In manufacturing, however, product disposition often occurs at a higher level. For example, disposition may be based on the average of several measurements across a wafer; in this case, individual chip measurements are less important than the wafer average. It is, therefore, important for a successful metrology system to correlate well to the RMS on a “by wafer” level, where each data pair in a correlation plot represents the average of all measurements across a wafer for a given structure.

Because of this importance, the data were also organized into “by wafer” plots. Each of the four wafers, however, was processed in an identical manner. This presented a problem because there was little variation in the average CD across the wafers. A rigorous TMU analysis requires the artifacts to represent a wide range 50

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of the parameter being measured. In order to accomplish this, the concept of the “virtual” wafer is introduced and used to construct “by wafer” plots. For this experiment, a virtual wafer consists of all the chips from all four wafers that are at the same location on the wafer. For example, chip 1 may be located on the second row and third column on each wafer. Conceptually, grouping the chip 1’s from all four wafers makes up a virtual wafer. There are eight virtual wafers in this experiment (one for each measured chip on a physical wafer), each consisting of four chips (one for each physical wafer). A conceptual difference between physical and virtual wafers is shown in Figure 5.

A1 A2 A3 A4 A5 A6 A7 A8

B1 B2 B3 B4 B5 B6 B7 B8

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Figure 5. Physical wafers A, B, C, and D (solid lines) versus virtual wafers 1 through 8 (dashed lines). Each letter/number pair (A1, B1, etc.) represents a measured chip.

Because each virtual wafer was processed with a different set of lithography conditions (dose and focus), the range of CDs generated by averaging across each virtual wafer is sufficient to conduct a rigorous TMU analysis. Furthermore, it is noted that every chip within a virtual wafer was processed with the same lithography conditions; similarly, in manufacturing every chip within a physical wafer is usually processed with the same lithography conditions. Although in manufacturing each wafer is processed in the same nominal way, slight differences in processing due to variations in etch, lithography, etc. cause average CDs to drift from wafer to wafer. This manufacturing reality is represented in this experiment by the different lithography conditions used to process each virtual wafer. Thus, representing by wafer results in terms of virtual wafers has benefits beyond that of the larger range of wafer average CDs. All “by wafer” correlations shown in this paper refer to virtual wafers.

Display of TMU results TMU analysis has improved since it was first disclosed

by Sendelbach and Archie.4 One improvement,

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described earlier, shows how an uncertainty in TMU can be quantified. Another improvement has been described by Sendelbach et. al.5 and reveals a more rigorous methodology for determining the uncertainty of the RMS. This is critical to obtaining an accurate estimate of TMU when the TMU is less than or approximately equal to the uncertainty of the RMS. Both improvements are used in the data analysis and results in this article.

Although the Mandel slope of the best-fit line and the average offset are important metrics in TMU analysis, they are not used in this article because of the large amount of data and our desire to concentrate on general TMU trends.

Results

CD-SEM versus electrical measurements First, the correlation between the CD-SEM and the electrical measurements will be discussed. Figure 6 summarizes this correlation using “by chip” TMU analysis. Some explanation of this chart and other similar charts that will be presented in this article is necessary. The convention that is used here is that the first metrology method listed in the chart title is the TuT, while the second is the RMS. The x-axis describes which two structures were measured. The structure listed before the “vs.” was measured by the TuT, and the structure after the “vs.” was measured by the RMS. Although four CD-SEM measurements (four reps) were made on each grating, only one of these reps was used in this TMU analysis. This method more closely mimics manufacturing than if all four reps were averaged together; that is, generally only one measurement per structure is collected on product lots. Later, these TMU results will be directly compared to similar results using scatterometry. Using only one rep means that single CD-SEM measurements will be compared to single scatterometry measurements, resulting in an

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The question then becomes, “Which rep should be used?”

The best answer is to use each rep in a separate TMU analysis, thus generating four TMUs per structure. The TMUs should then be converted to variances (recall that TMU is a 3σ number). The variances should be averaged, and finally converted back to a single TMU. This technique was used here. “By wafer” results comparing CD-SEM to electrical measurements are displayed in Figure 7.

Similar results are seen in both Figures 6 and 7. As the structure pitch increases, the correlation to electrical measurements improves. This makes sense because the Lpoly structures have a large pitch (770 nm). Subtle changes in structure geometry as a function of pitch are the likely explanation of why CD-SEM measurements on structures correlate better to electrical structure measurements when both structures have similar pitches. An exception is noted with the 595 nm pitch scatterometry structure. It is believed that this could be due to the slightly re-entrant profile of this structure.

Unlike a sidewall angle with less than 90º, slight changes in sidewall angle on re-entrant profiles are not detected by the top-down metrology used by the CD-SEM. The electrical measurement is sensitive to such angle changes on re-entrant profiles because of the capacitive nature of the measurement. Not surprisingly, the correlation is best when the CD-SEM is used to measure the actual Lpoly structures that are measured electrically.

Our final conclusion is that the correlation to pLpoly electrical measurements is usually better than the correlation to nLpoly measurements. This is unexpected, and therefore interesting, because the scatterometry and dispo structures are n doped. The most likely explanation for this phenomenon is described in the conclusions.

CD-SEM vs. Electrical Measurements - Single Rep By Chip

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apples-to-apples comparison. No attempt is made here to adjust the comparison for throughput or cost of ownership, although such an analysis would be extremely useful.

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Figure 7. CD-SEM versus electrical measurement, by wafer.

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Scatterometry versus CD-SEM measurements

Scatterometry versus electrical measurements

For this comparison, the CD-SEM is the RMS. Therefore, it was important to take advantage of all four CD-SEM reps. Thus, all four reps were averaged to represent the CD-SEM measurement for each structure and chip.

These averages were then used in the TMU analysis for both the by chip and by wafer correlations. Figures 8 and 9 show the results.

Of course, the RMS in this situation is the electrical measurement. The scatterometer appears to correlate better to the CD-SEM measurement as the pitch decreases; however, the CD-SEM measurements on the scatterometer structures seem to correlate better to the electrical measurements as the pitch increases. Both effects should influence the correlation between scatterometry and electrical measurements. But which of these conflicting influences is stronger? Figures 10 and 11 provide the answers.

Because larger pitch structures have a larger space:line ratio than smaller pitch structures with similar linewidths, these structures have a smaller signal-to-noise ratio when measured with a scatterometer. It would therefore be expected that the correlation would worsen as the pitch increases. Results are consistent with this correlation, but not definitive. The correlation for the 595 nm structure would probably be closer if the re-entrant profile effect described in the previous section occurred, and the CD-SEM better detected these profile changes. In this case, the correlation as a function of pitch would be flatter, and within the limits shown by the TMU uncertainty bars.

No clear scatterometry dependence on pitch is observed when comparing to either the nLpoly or the pLpoly electrical measurements. The influences mentioned in the previous paragraph, if real, seem to cancel each other.

So it is insignificant as to which structure is chosen for inline monitoring of CD using scatterometry. As for the CD-SEM, however, correlation to pLpoly electrical measurements is again better than correlation to nLpoly measurements. ents -- By Chip

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4.0

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TMU

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Figure 11. Scatterometr y versus electrical measurements, by wafer.

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Scatterometry and CD-SEM versus electrical measurements

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In order to determine whether the scatterometer or the CD-SEM tracks electrical measurements better, the data from previous sections of this article are reorganized into four charts, shown in Figures 12-15. Two charts show “by chip” results, and two show “by wafer” results.

One of each of these two charts compares performance to only nLpoly electrical measurements; the other two charts compare to only pLpoly measurements. In each of these charts, the first eight TMUs are paired such that the left TMU displays the correlation between scatterometry and the electrical measurement, and the right TMU displays the correlation between the CD-SEM and the electrical measurement for the exact same structure. The last three TMUs in each chart display only CD-SEM results on the two Lpoly structures and the dispo structure.

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Figure 14. Scatterometry and CD-SEM versus nL poly electrical measurement by wafer.

12.0 10.0 8.0

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In these figures, we see that in most cases there is a large overlap in the uncertainty bars between the scatterometry and CD-SEM TMUs. Thus, the scatterometer performs about as well as the CD-SEM. A larger data set would reduce the size of the uncertainty bars, leading to a more definitive conclusion about which metrology method performs better.

Figure 15. Scatterometry and CD-SEM versus pL poly electrical measurement by wafer.

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CD-SEM versus CD-SEM measurements

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Figure 12. Scatterometry and CD-SEM versus nL poly electrical measurement by chip.

12.0 10.0 8.0

It is interesting to also compare CD-SEM measurements on various structures. Specifically, how well do CD-SEM measurements on the Lpoly structures compare to CD-SEM measurements on the other structures? This is a measure of how well these structures match each other. In order to answer this question, all four CD-SEM reps were averaged; these averages were then used in by chip and by wafer analyses. The measurements on the Lpoly structures were used as the RMS. The exception to this is when the two Lpoly structure measurements were compared to each other. In this case, it is unclear which measurement should serve as the RMS. The answer is to iterate the TMU analysis until the TMU equals the RMS uncertainty. This way, the TuT and the RMS share the measurement uncertainty equally.

Upper limit 6.0

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Figure 13. Scatterometry and CD-SEM versus pL poly electrical measurement by chip.

Results are displayed in Figures 16 and 17, and are similar to the results obtained when comparing CD-SEM measurements to electrical measurements. This includes an increase in correlation as the pitch increases, with the 595 nm scatterometry structure being an exception, as well as a better correlation to pLpoly structures than nLpoly in most instances. The Lpoly structures correlate very well to each other.

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Figure 16. CD-SEM versus CD-SEM measurements by chip.

Figure 17. CD-SEM versus CD-SEM measurements by wafer.

Sources of correlation loss

RMS uncertainty, even though this is not considered the RMS measurement. The measurement uncertainty of the actual RMS (the CD-SEM measurements on the electrical structure) was already subtracted during the TMU analysis, and so does not need to be considered again. That is, it is no longer a source of the correlation loss between the two types of structures because the TMU analysis already removed this component. To calculate the structure-to-structure variation, subtract the measurement error of the CD-SEM when measuring the scatterometry structures from the TMU determined between the CD-SEM measurements on the two sets of structures, making sure to work with variances when subtracting. This methodology was used here. Although this source of correlation loss is not directly caused by the scatterometry measurement, it is indirectly related, because the scatterometer is forced to measure a structure other than the one being measured electrically.

A natural question is to ask what specifically causes the loss of correlation between the scatterometry measurements and the electrical measurements. This “correlation loss” can be broken down into three separate sources.

The first is from the scatterometry measurement itself.

This component is determined by the correlation between scatterometry measurements and CD-SEM measurements on the scatterometry structures. It may seem that this correlation has a component of correlation loss caused by the CD-SEM measurement as well as the scatterometry measurement. But the TMU analysis is constructed so that the uncertainty of the RMS (here, the CD-SEM) is removed. However, if the RMS does not act as a good reference (e.g., it does not properly track process changes), then the TMU analysis is not being carried out properly. In this case, some of the TMU may be attributed to the RMS. For the correlation between scatterometry and CD-SEM measurements, this could be the case for the 595 nm pitch structure (as described previously), and perhaps also the 507 nm pitch structure.

Therefore, the reader should be aware of this possible contribution when reviewing the scatterometry component of the total correlation loss.

The second source is from the variation between scatterometry structures and electrical Lpoly structures.

The most common causes of this variation are due to the reticle, as well as the lithography and etch processes. To measure this source, the correlation between CD-SEM measurements on the scatterometry structures and CD-SEM measurements on the Lpoly structures must first be determined. There are actually two sources of correlation loss here. One is from the structure-to-structure variation itself, which is the component we are trying to determine. The other is from the measurement error of the CD-SEM when measuring the scatterometry structures. This can be estimated by using the methodology described by Sendelbach et. al.5 to determine the 54

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The last source of correlation loss is from the processing that occurs after the scatterometry measurement and before the electrical measurement. Such processing includes, for example, implantation and hot processing, and will be referred to as “post-processing.” The best way to determine this is by finding the correlation between CD-SEM measurements on the Lpoly structures and electrical measurements on those same structures.

Once again, this correlation actually has two components: one caused by the post-processing, and one caused by the CD-SEM measurement of the Lpoly structure. As before, this CD-SEM portion can be determined using the same methodology referenced in the previous paragraph. But in this situation, the CD-SEM portion was not removed from the total correlation because 75 percent of the time, this portion was larger than the TMU between the CD-SEM measurement and the electrical measurement. This can happen because all of these quantities are estimates. The quality of these estimates depends on the number of measurements used to

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This work shows how critical TMU analysis, including the use of TMU uncertainty bars, is to assessing, optimizing, and comparing metrology systems. It is doubtful that so many questions could have been answered without the use of this methodology. TMU analysis helped reveal some interesting results and clear trends among different metrology techniques. One of the most important results was that scatterometry measurements were comparable to CD-SEM measurements at predicting electrical measurements. Perhaps the most surprising trend was that both scatterometry and CD-SEM measurements on the n doped scatterometry and disposition structures correlated better to the p doped Lpoly structures than the n doped Lpoly structures.

We expected the opposite to be true, or at least that the correlations would be equivalent. Upon analysis, several possible explanations became evident. The most likely one came from an explanation of the gate etch process. During this process, the implanted portion of the n doped silicon is subject to more profile variation than the implanted portion of the p doped silicon due to the difference in how the etch chemistry reacts with

5.0

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It can be seen that the post-processing component is, in most cases, the smallest source of correlation loss, and the scatterometry component is usually the largest source. The post-processing and structure-to-structure variation components are difficult to improve. If they were the primary sources of correlation loss, then it would be difficult to further improve the scatterometry to electrical measurement correlation. But the scatterometry component is much easier to improve. In fact, three paths for improvement of the scatterometry performance for this project were described in the earlier section that discussed modeling and measurement techniques not explored: the use of current wafers to measure film properties and thicknesses near the scatterometry structure, the use of wavelengths down to 190 nm to gather more information, and the use of

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Conclusions

5

These methods were used to calculate the sources of correlation loss between the scatterometry measurements and the electrical measurements. The sources were expressed as TMU variances and plotted in Figures 18 and 19. Because the TMUs were determined using both by chip and by wafer analyses, the TMU variances were plotted using each of these analyses.

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models that are individually tailored to the pitch of the structure they are measuring. The first two of these improvements were not available at the time of this evaluation, while the third improvement was not used because we wanted to see how well the scatterometry modeling could perform under the “constant across pitch� constraint. Thus, the result is that the scatterometry-to-electrical correlation can be further improved beyond its already impressive results.

24

determine the estimate. An insufficient amount of data was collected in this project to do this exercise completely and still avoid non-physical results such as this. This attests to the need for thorough and extensive metrology in the semiconductor industry in order to properly answer the kinds of questions raised in this work. This last source of correlation loss is a weakness of any inline measurement (scatterometry, CD-SEM, CD-AFM, etc.), but is not caused by the inline measurement.

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Figure 18. Sources of correlation loss between scatterometr y and

Figure 19. Sources of correlation loss between scatterometr y and

electrical measurements, by chip. The CD-SEM is suspected to be a

electrical measurements, by wafer. The CD-SEM is suspected to be a

poor RMS for the 507 and 595 cases; if this is true, then some of the

poor RMS for the 507 and 595 cases; if this is true, then some of the

scatterometr y component shown for these pitches were caused by the

scatterometr y component shown for these pitches were caused by the

CD-SEM.

CD-SEM.

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these two regions. The CD-SEM and scatterometry measurements are more sensitive to this profile change than the capacitive electrical measurements, which are only sensitive to the bottom CD of the silicon. This difference in profile variation, combined with the metrology sensitivity differences, worsens the correlations to the nLpoly electrical measurement.

Another interesting conclusion is that the scatterometry measurement appears to be the primary source of correlation loss between scatterometry and electrical measurements; “post-processing” caused by intermediate processes and structure-to-structure variation are secondary sources. Because the scatterometry component is the easiest to improve, further improvement in the scatterometry-to-electrical correlation can be reasonably attained.

It is noteworthy that the Lpoly electrical measurement has its limitations as a reference measurement system.

The main limitation is from the determination of Cfringe, the fringe capacitance. This is calculated by simulation and may have a substantial error associated with it. As linewidths shrink, this error plays a larger and larger role in the total error of the Lpoly measurement. The loss of confidence in this measurement that is starting to build may soon force metrologists to more frequently compare inline measurements to electrical measurements collected after the chip completion.

In order to be fair to the CD-SEM, it must be emphasized that not only scatterometers, but also CD-SEMs, are constantly improving. In fact, the CD-SEM model that was used in this project is now approximately one to two generations behind the latest model. Thus, comparing scatterometry to the CD-SEM is an exercise worth revisiting periodically to ensure that both remain competitive.

This work demonstrates the “proof-of-concept” that scatterometry measurements can be as good as CD-SEM measurements. And since scatterometry has several benefits over CD-SEMs, this technology is clearly a viable alternative to the CD-SEM for inline CD metrology. However, additional data are needed to more conclusively address this important topic.

Acknowledgements

The authors would like to thank Ishtiaq Ahsan of IBM

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for very helpful discussions about Lpoly. We would also like to thank Jesus Rivas, Bill Henderson, and Umar Whitney of KLA-Tencor for providing significant technical contribution to this project and for reviewing the manuscript.

This article was previously published in the SPIE Proceedings, vol. 5375, pp. 550-563.

References 1. J. Kye and H. Levinson, “Electrical linewidth metrology for next generation lithography,” Proceedings of SPIE, Vol. 4344, pp. 637-643, 2001.

2. S. Hodges, C. Lin, D. Burrows, R. Chiao, R. Peters, S. Rangarajan, K. Bhatia, and S. Lakkapragada, “Improved gate process control at the 130 nm node using spectroscopic ellipsometry based profile metrology”, Metrology, Inspection, and Process Control for Microlithography XVII, Daniel J. Herr, Editor, Proceedings of SPIE, Vol. 5038, pp. 215-223, 2003.

3. E. Solecky, J. Mayer, and C. Archie, “Improving sub150nm lithography and etch CD-SEM correlations to AFM and electrical test”, Proceedings of SPIE, Vol. 4689, pp 473-483, 2002.

4. M. Sendelbach and C. Archie, “Scatterometry measurement precision and accuracy below 70 nm,” Metrology, Inspection, and Process Control for Microlithography XVII, Daniel J. Herr, Editor, Proceedings of SPIE, Vol. 5038, pp.

224-238, 2003.

5. M. Sendelbach, W. Natzle, C. Archie, B. Banke, D. Prager, D. Engelhard, J. Ferns, A. Yamashita, M. Funk, F. Higuchi, and M. Tomoyasu, “Feedforward of mask open measurements on an integrated scatterometer to improve gate linewidth control,” Metrology, Inspection, and Process Control for Microlithography XVIII, Proceedings of SPIE, Vol. 5375, 2004.

All performance data contained in this publication was obtained in a specific environment and is presented as an illustration. The results obtained in other operating environments may vary. ‡ TMU and Uncertainty of TMU are patent pending