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Process Parametrics F

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Pattern Quality Confirmation: Increased CD SEM Sensitivity to Yield Limiting Process Excursions

by Richard Dare, Lucent Technologies Amir Azordegan and John Miller, KLA-Tencor Corporation

Feature size has a direct correlation to device performance, process yield, and reliability. Quantifying feature size with a CD SEM is, therefore, an important part of process control. As devices get smaller, feature shape is becoming equally important due to its effect on the transfer functions from photo to etch and the electrical properties of the device after etch. Measurements of the shape of sampled structures are necessary to develop and improve the correlation between process changes and subsequent yield and device reliability. Ideally, measurements of feature size and shape would occur on a single metrology platform. However, the process of measuring feature size with a CD SEM is typically one of reduction, with large amounts of information contained in images and linescans reduced to a single number. Critical information about feature shape is lost, and process excursions are not identified in a timely manner.

Users of top-down CD SEMs know that features which are noticeably different in cross section can produce similar CDs. An example of this would be two lines with similar bottom dimensions but with different sidewall slopes as shown in Figure 1.

Traditional CD SEM measurements would detect the increase in the electron signal as the feature is scanned at the onset of the sidewall as the feature “edge”. Since the bottom dimension of the line cross section is similar for these features, this “edge” in the electron signal will give similar CDs for the two lines. Information about the sidewall slope that is contained in the images and linescans produced by the SEM is not utilized.

KLA-Tencor has developed Pattern Quality Confirmation (pQC™) to utilize the feature shape information lost in the reduction of a complex feature scan to a single CD measurement. pQC provides value in the form of additional metrics that can be used to track pattern fidelity. These metrics are 46

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Optimal F-E

CD=543 nm

Strong Defocus

CD=535 nm

F i g u re 1. Optimal and defocussed i-line 0.5 µm ADI metal 1 line CD SEM images and linescans. ρ indicates the correlation between the non-optimal an d optimal images (and linescans). While the defocussed line CD i s within spec, it is clear tha t the defocussed lin e suffers fr om a serious degradati on i n s idewa ll slope. This shape anomaly is reflected in the pQC corr elati on score (per fect correlati on = 100).

derived from the correlation of measured features to user-defined standards for the feature in the form of linescans or images.

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pQC experiment

To investigate the impact of feature quality on device yield, Lucent Technologies implemented KLA-Tencor’s pQC software on an 8100 CD SEM used for in-line process control. The investigation of pQC in a production environment was the central goal of this experiment.

It was required that pQC function during the execution of standard production recipes, with little or no decrease in the throughput of the CD SEM. Lucent produced test wafers for gate level, contact, and the first metal level using a device designed for testing and engineering work. Stepper focus and exposure were varied in the production of these wafers to produce a range of feature shapes that could occur as the result of process drift.

After measurement on the KLA 8100, the wafers were processed to completion and sent for electrical testing and yield analysis. Images from cross sections of some of the lines and contact holes were also obtained for comparison to pQC results.

F i g u re 2. Ga te level dimensions and pQC s cores as a f unct ion of stepper focus. Heavy lin es i ndicate lower a nd upper C D values

Results

assuming a 5 perc ent contr ol limit.

Gate Level: Since device speed is determined by the dimensions at the gate level, tight control of size and shape of the resulting feature is essential. The pQC analysis provided a correlation score between the linescans and images obtained from the test features, and a well-formed feature produced at the center of the process window. pQC data and CDs for the gate level photo resist line are shown in Figure 2. CD and pQC data were compared to the electrical performance of the device. The prediction of optimal yield was better for the pQC scores alone, than for the CD measurement alone; however, the combination of pQC and CD data provided the best prediction of electrical yield. The power of the pQC analysis is clear in Figure 3, where CDs and pQC correlation scores are shown for the etched polysilicon gate. The pQC scores show a single peak corresponding to the chips with adequate yield, while the CD data shows that two chips with nearly identical CDs inside the five percent process window had different yield results.

First Contact: The study focused on using pQC to distinguish between deformed or closed contacts and optimal, open contacts. The pQC scores were produced through 2-D image correlation to a well-formed contact.

Measurements of the contact resistance for these features showed that the pQC scores for closed and open contacts were well separated in a bimodal distribution. This allowed for classification of closed contacts in the operation of the SEM recipe. Comparison of cross sectional

F i g u re 3. Line size a nd profi le variati on o f etched po lys ilicon as a fun ct ion of ex posure dos e. Measurements fr om chips with acceptab le electr ical chara cteristics are circ l e d .

images of the contacts to the pQC scores show that pQC can distinguish between closed and open contacts even when these contacts have similar CD values (Figure 4).

The classification of open and closed contacts can occur in-line during the execution of the standard, automated Spring 2000

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CD recipe augmented with the pQC functionality. The output of the pQC analysis would be in the form of a correlation score, or a simple “open/closed” output that would reduce the need for pQC interpretation once the CD SEM recipe had finished execution.

is accomplished under full automation with little or no impact on CD SEM throughput, the results obtained in this experiment could be easily obtained in any production setting. With proper implementation, pQC can be used to optimize process margins and improve device yield.

F i g u r e 4. Cr oss section i mages of closed a nd open con tacts. The contacts have si mlia r CD sizes, but pQC scor es that diff er by >2X.

First Metal Level: In photolithography, the first metal level is the most challenging for this process, because it has a relatively high aspect ratio (3.3: 1) and it is printed on a TiN-Al-TiN layer which makes it sensitive to focus and exposure variations. This level showed the best correlation for pQC to wafer yield, as indicated in Figure 5. Use of the pQC scores and traditional CDs together resulted in an average gain of 19 percent in predicting yield. The CD alone proved insufficient in predicting good yield in two areas. In the first case, feature CDs were measured to be within specification, but the device did not yield. In the second case, feature CDs were measured to be out-of-spec, yet the device did perform adequately. A combination of traditional CDs and pQC scores had the best correlation to yield by decreasing the number of “missed” devices and increasing the number of acceptable ones. Electrical tests did a poor job in estimating final device yield for this layer.

Conclusion

The study demonstrated that pQC measurements combined with traditional CD data offer an improved method for detecting process variations. They also serve as a means for checking the validity of top-down CD measurements. This has shown significant impact on reducing the risk of sending ahead poorly processed wafers that will negatively affect device yield. As pQC

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F i g u re 5. Avera ge of pre-etch and p ost-etch process measurements for first metal showing the a dvantage of using pQC mea suremen ts along with CD mea suremen ts to predict device yield.

Acknowledgement

The authors thank Robert Griffin for all the fruitful discussions we had during this study, and for his initialization of the yield optimization procedure. We also thank David Goodstein from KLA-Tencor for providing the analysis software, Joe Cassano for the electrical testing, Zbig Kozlowski for the yield data analysis, Robert Criscuolo for the wafer starts, and Debbie Yancho for cross-sectional SEM work.

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