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Identifying Process Drift with CD SEMs Getting more than critical dimensions from SEM linescans and images with correlation scoring

by David M. Goodstein, Applications Engineer

In situations where CD-only-based monitoring of process integrity proves inadequate, the CD SEM continues to provide essential monitoring capability through the use of linescan and image correlation metrics. This additional information is extracted from the same line scans and images acquired in the course of standard automated CD measurement.

Consequently, there is almost no impact on throughput. The high sensitivity of these metrics to even small degrees of process variability suggests they will play an important role in all demanding CD SEM-based process monitoring and control applications.

CD SEMs play an essential role in photolithography and etch process characterization and monitoring. Post-develop (DI) and post-etch (FI) monitoring of device critical dimensions (CD) as well as fast, thorough characterization of processes and process equipment are routinely handled by advanced high-throughput CD SEMs. Realtime process monitoring is especially important to catch yield-compromising process variations as soon as possible, before a significant and costly fraction of in-process wafers are affected. While CD is the standard metric on which CD SEM monitoring is based, additional and potentially more sensitive metrics are available from the very same tool.

To see why more sensitive monitoring metrics might be necessary even at large design rules, consider the CD variation of an i-line resist isolated line on metal as a function of stepper defocus and exposure (figure 1). With a nominal CD of approximately 540 nm (at 0 Âľm defocus, 190 mJ exposure), a DI monitor that flagged resist-line CD deviations greater than five percent would still allow significant process variation (figure 2).

Process drift of this magnitude might well impact post-etch metal line CD and overall line integrity, compromising device performance and total yield. This can be avoided

with tighter process monitoring at the post-develop stage, using non-CD-based metrics.

Limitations of CD-only monitoring

It should not be surprising that not all process drift can be identified by changes in critical dimension.

Interconnects, gates, contacts and vias are fundamentally three-dimensional structures, and characterizing them solely in terms of CD (which is itself a function of the measurement algorithm applied to the CD SEM image or linescan) is necessarily a simplification. If a device feature simply scales uniformly with process drift, then CD is an adequate metric. However, this is often not the case.

The limitations of CD-only characterization and monitoring are evident in figure 3, where isolated end-of-line structures are imaged by a CD SEM at 75kX magnification. Measurement linescans, acquired near the middle of the line, are also shown. The resist line printed at 1.2 Âľm defocus and 200 mJ exposure (bottom image) measures at 535 nm, a CD deviation of less than two percent from nominal (543 nm). Such a line would pass most CD-only monitors, where process windows are typically 10 percent of nominal. However, when compared to the line printed at optimal focus and exposure (top image), the non-optimal line clearly suffers from reduced sidewall steepness (wider, but lower intensity edges) and variable resist thickness (bright fringes across the line). These changes can lead to an appreciably Autumn 1998

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feature under inspection to the optimal one I am trying to manufacture?” For CD SEMs, the most accessible metric of feature fidelity is linescan or image correlation, calculated against a reference or optimal template. It utilizes the same linescans and images that are acquired during the course of auto-

correlation, and CD correlation (defined as one minus the absolute fractional CD deviation from optimal) are plotted for varying defocus at optimal focus (figure 5a) and varying exposure at optimal focus (figure 5b). In both cases, correlation scoring provides a more sensitive measure of process drift than CD.

Figure 1. Bosung plots of isolated line CD vs. focus (in µm) for i-line resist on metal 1.

different etch transfer function, which may unfavorably impact the resultant metal line and, therefore, the etch process yield.

The key to catching these types of process variations is to monitor not only feature CD, but a metric that reflects overall feature fidelity as well; such a metric should answer the question: “How similar is the

Optimal Field (0.0 µm, 190 mJ) CD = 543 nm

Inspection Field (-1.2 µm, 200 mJ) CD = 535 nm

Correlation

Figure 3. A comparison of end-of-line images and measurement linescans from the isolated resist lines of figures 1 and 2. Focus and exposure conditions are indicated in parentheses.

mated measurement and has the added advantage that, unlike CD measurement, it is an algorithmindependent metric of a feature’s faithfulness to a standard.

Figure 2. Wafer map showing deviation of i-line resist on metal 1 isolated line CD from optimal CD as focus and exposure var y. The yellow field corresponds to optimal focus and exposure, with a CD of 543 nm. The green fields define the range of process conditions that yield resist-line critical dimensions within five percent of 543 nm.

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The one-dimensional correlation between the non-optimal and optimal measurement linescans (bottom right), and the twodimensional correlation between the non-optimal and optimal end-of-line images (bottom left), are displayed in figure 3. These low correlations (compared to those in figure 4) should raise a red flag that significant process drift has occurred, even though the CDs at this process step remain in spec.

Further evidence of the potential power of correlation-based monitoring is shown in figures 5a and 5b, where image correlation, linescan

Yield Management Solutions

The effect of establishing an 80 percent threshold (20 percent deviation) on linescan correlation for this layer is shown in figure 6.

Correlation scoring clearly facilitates more precise monitoring of process drift than CD measurement alone.

Fully automated correlation monitoring

The power of correlation-based monitoring would be of little benefit if it could not be implemented with the same level of automation and speed as CD-based monitoring.

In fact, linescan and image correlation monitoring on the KLA-Tencor 8100XP can be integrated seamlessly into normal automated CD measurement, and provides: • Real-time calculation, reporting and output to measurement data

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linescan- and image-based characterization methods,1 will be a critical part of every successful CD SEM-based yield monitoring system.

Optimal Field (0.0 µm, 190 mJ) CD = 543 nm

Inspection Field (-0.3 µm, 190 mJ) CD = 558 nm

Correlation

Figure 4. A comparison of image and measurement linescans between optimal and near-optimal resist lines. Note the significantly higher linescan and image correlations as compared to those in figure 3.

files of linescan and image correlations against user-defined optimal templates.

• Automatic acquisition of sub-optimal linescans and images, based on user-defined thresholds, for subsequent review.

This is accomplished with nearzero impact on throughput in automation.

Correlation monitoring is, by no means, limited to the example discussed here. Dense lines, contacts, other layers, and the smallest design

rules are all accommodated in the current implementation on the 8100XP. Depending on the application, acceptable correlation thresholds can be determined and subsequently enforced in conjunction with standard CD monitoring.

When fully integrated into CD SEM-based, closed-loop advanced process control, correlation provides an even more powerful tool for maintaining lithographic process integrity and achieving maximum yield. As device geometries continue to shrink and process windows continue to narrow, correlation monitoring, as well as other

Figure 6. Wafer map showing linescan correlation score grouping as a function of focus and exposure dose. The yellow field corresponds to optimal focus and exposure. Taken together, all colored fields define the range of process conditions that yield at least 80 percent linescan correlation.

1 J.M. McIntosh, et. al., “Approach to CD SEM Metrology Utilizing the Full Waveform Signal,” SPIE Proc., 1998.

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Figure 5. a) Variation of isolated-line CD correlation (one minus absolute fractional deviation from nominal), image and linescan correlation at optimal exposure (190 mJ) as a function of defocus. b) Variation of isolated-line CD, image and linescan correlation at optimal focus (0.0 µm) as a function of exposure dose.

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The author wishes to thank Bhanwar Singh and Bryan Choo of Advanced Micro Devices for providing the initial impetus for this investigation, as well as providing the focus-exposure wafers used to generate these results.

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Conclusions

Using the excursion cost model enabled IBM to quantify the benefits of classification. The exercise revealed that classification, in general, is a vital part of a cost-efficient, in-line monitor strategy. In addition, classification metrics, such as the accuracy and review time associated with classification, determine the cost of an excursion.

The key advantages in classification accuracy and time-to-results substantiate the need for on-line ADC as a replacement for manual defect classification on the ADI in-line defect monitor. The revenue losses associated with excursions are reduced by an estimated $42,000 per week by implementing IMPACT/Online ADC at the ADI in-line monitor location. The success at this process monitor, along with that of the entire beta evaluation, has motivated IBM to pursue the implementation of more production monitors using IMPACT/ Online ADC. Future IBM interests include SEMbased ADC and ADC on laser-scattering defect inspection tools. The optical limitations of identifying defects < 0.35 um in size combined with the constant reduction in critical dimensions that come with new process technologies favor an SEM-based ADC solution.

1 Louis Breaux and Dave Kolar, “Automatic Defect

Classification for Effective Yield Management”, Solid State Technology, December 1996, pp. 89 96.

2 Nurani, R. K., R. Akella, A.J. Strojwas,

R.Wallace, M.G. McIntyre, J.Shield, I.Emami, “Development of an Optimal Sampling Strategy for Wafer Inspection”, International Symposium on Semiconductor Manufacturing Proceedings, Tokyo, Japan, June 1994.

3 Wang, E.H., “An Integrated Framework for

Yield Learning in Semiconductor Manufacturing”, Stanford University Ph.D. Dissertation, May 1997, Chapter 3.

4 Wang, E.H. and D. Fletcher, “Optimal Wafer

Inspection Strategy with Learning Effects”, ASMC, October 1996.

6 The Competitive Semiconductor Manufacturing

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Research Group consists of professors and Ph.D. candidates from UC Berkley, Stanford and Carnegie Mellon Universities.

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5 Nurani, R.K., R. Akella and A. J. Strojwas, “In-

line Defect Sampling Methodology in Yield Management: An Integrated Framework”, IEEE Transcript On Semiconductor Mfg. 1996.

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This article was first presented as a paper at the Advanced Semiconductor Manufacturing Conference and Workshop (IEEE/SEMI), Cambridge, MA., September 10-12, 1997.

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