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The More and Less of Effective Overlay Control Ming Yeon Hung, Taiwan Semiconductor Manufacturing Company Xuemei Chen, Kelly Kuo, Steven Fu, Viral Hazari, Kevin Monahan, Mike Slessor, and Amir Lev, KLA-Tencor Corporation George Shanthikumar, Zhoujie Mao, and Shiming Deng, Universtity of California at Berkeley

With shrinking design rules and the transition to 300 mm wafers, the risk and cost associated with process excursions become more severe. With the increased number and value of transistors per wafer, any process or product excursion that goes undetected or is not forestalled, implies significant material at risk and unnecessary production cost. Therefore, a systematic approach to excursion management that ensures effective detection, identification, and reduction of process excursions is essential for realizing the productivity and cost benefits of the technology shifts. In this article, we describe excursion management as applied to overlay in lithography, in the context of a total lithography metrology ROI analysis framework for 300 mm high volume production.

Introduction

In addition to capital investment such as tool and footprint cost, the potential revenue gained through effective excursion control and process control must be accounted for in a total lithography metrology ROI cost model. Effective excursion control reduces the potential yield loss due to excursions by detecting and resolving process excursions efficiently. Effective process control improves the revenue through the variance reduction achieved with efficient feedback. Both are affected by the chosen sampling plans and time to response afforded by the fab metrology configurations.

Effective excursion management needs to achieve two objectives: 1) Timely detection of yield-impacting excursions, which is dependent on the following criteria: • Optimized sampling based on proper separation and characterization of excursion and baseline statistics

• Shorter excursion detection delay by improving metrology queue and measurement time 2) Excursion risk reduction, which involves the following: • Characterizing excursion types, and eliminating sources for excursion effectively • Reducing lots exposed to excursion through faster time to results • Reducing excursion frequency by improving baseline variance distribution It becomes apparent that sufficient sampling and shorter cycle time are two major factors that contribute to the effectiveness of excursion control, and it is important that the respective metrology needs be quantified and validated using actual fab production data.

In the following sections, we present the overlay excursion management strategies and analysis steps, and validate the methodology with 300 mm fab overlay data. In particular, the wafer-to-wafer and within-wafer sampling needs, and impact of cycle time on excursion risks will be addressed.

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Overlay excursion management methodology

An effective overlay excursion management strategy is formulated through comprehensive data collection and analysis of historical data from a relatively stable production line. The following steps are involved.

First, the spatial and temporal distributions of the data are examined for all nested levels of interests, which include: (1) lot means, wafer means, field means, and site means; (2) site-to-site sample variances within field or wafer, field-to-field sample variances within wafer, and wafer-to-wafer sample variance within lot. Appropriate distributions of baseline data are then determined to accommodate the majority of the data set. Excursion set of the historical data is then identified and separated from the baseline population statistically with 95 percent confidence level. From examination of production overlay data, we found that the generally assumed normal distribution for means and chi-square distribution for variances no longer holds in many cases, and a more genericallyshaped probability distribution such as the gamma distribution represents the baseline population better.

Once the baseline and excursion populations are separated, baseline and excursion statistics are estimated.

Baseline statistics includes variance components at each nested level (lot, wafer, field, site). Excursion statistics includes excursion types, each with its own magnitude and mean time to excursion.

In this study, we developed a spatial-temporal stochastic model of overlay variance components, incorporating the spatial dependencies of overlay errors. The overlay variance estimation model differs from the generalized nested ANOVA model used in several aspects. 1 The new model incorporates a well-formulated spatial model that corresponds to the exposure tool correction effects.

The unbiased estimates of the random variances of the correctables and residuals are formulated, and the systematic spatial variability due to correctables and residuals are also estimated. In a complete form, the total overlay variance is broken down into site, field, wafer, and lot levels, with the site and field levels further decomposed into systematic and random components.

The systematic and random components each contain effects accounted for by scanner correctables and residuals.

Excursion data is classified by exploring the difference vectors between baseline overlay and excursion overlay.

Further examination is done to classify the excursions into correctables or residuals excursions. Through this 60

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study, we have generalized a gallery of the most commonly occurring overlay excursions. After classification, the statistics and frequencies of each type of excursions are calculated.

After the production data is completely characterized using the aforementioned analyses, cost optimization with respect to alternative sampling plans, control charts, metrology configurations with various cycle times is performed. The major cost component we are concerned with in excursion management is the material exposed to process excursions from the time a certain type of excursion occurs until the excursion event is detected and eliminated. It is a function of the excursion detection delay and mean time to excursion. In our model we assumed that, immediately after an excursion is detected, action is taken to prevent further material at risk due to the same type of excursion until the excursion source is identified and eliminated. In practice, this may not be the case, and additional material at risk is introduced.

Two factors contribute to the time to detection of an excursion. These are: (1) The beta risk associated with various sampling plans and control charts. Beta risk is the probability of missing an excursion, which is a function of the baseline and excursion statistics (types, frequencies, durations), the sampling intervals, and the type and limits of the control chart (2) The metrology queue delay and the overall cycle time delay from wafers exiting photo processing to measurement completion. This factor interacts with the chosen sampling plan and excursion frequencies in impacting the material processed during an excursion cycle To simplify the cost estimation with alternative sampling and metrology configuration scenarios, we developed a discrete event model emulating the fab baseline and excursion dynamics for a specified period. Metrology tool configuration and wafer flows specific to the fab operation are incorporated in the model. Outputs of the model include mean queuing time, number of false alarms, rework, and lots exposed to excursions between sampling and queuing delays, etc. If fab cost data such as average selling prices of the wafers is known, the total cost associated with excursions can be calculated, which can then be minimized with respect to the input factors such as sampling plans and metrology tool configurations to establish a cost optimal overlay excursion control strategy.

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300 mm fab results

Using the methods described above, we evaluated actual 300 mm fab overlay data from several critical layers, collected for a six-month period. The results are presented below.

Baseline and excursion signatures One and two-sided excursion removal algorithms are applied to separate the baseline and excursion populations. We then characterize the baseline and excursion signatures using overlay vector maps at the wafer and field levels. Figure 1 shows the baseline signature for the M1 layer.

Excursion lots are classified by examining the difference vector between the baseline and the excursion lot. In Figure 2, we show a gallery of excursion signatures we

F i g u re 2. Overla y e xcursion s ig nature s .

variances such as site-to-site and field-to-field variances are the highest components. Wafer-to-wafer variance is not a significant contributor to the total variance. The two layers have different variance distributions. The poly layer appears to have relatively larger site-to-site variance and lot-to-lot variance, although its total variance is smaller than M1.

We also examined the overlay residual baseline trend for M1, as shown in Figure 4. The within-wafer residual variance components are shown for four consecutive one-month periods. Field-to-field variance fluctuates over time, reflecting stepper and process changes; while site-to-site variance is relatively stable, primarily reflecting lens behavior.

Wafer-to-wafer sampling F i g u re 1. Examp le overla y baseline signa ture .

have observed. Some of the excursions, such as the first five types in Figure 2, are induced by prior processing steps; while others primary reflect scanner excursion, such as the excessive rotation error.

There are two objectives for optimizing wafer-to-wafer overlay sampling: (1) improve correction accuracy; (2) minimize material at risk.

To assess the wafer sampling needs for the first objective, a lot of 25 wafers from M1 is used as a reference for the lot correction. The worst overlay prediction

Baseline variance components Variance components analysis provides insights as to the distribution of the overlay errors, and helps identify and quantify the opportunity for variance reduction. In Figure 3, the baseline variance components for X overlay errors for the M1 and poly layers are shown. The variance components are expressed as the percentage of the total overlay variance. It can be seen that within-wafer

F i g u r e 3. Overlay b as eline variance components .

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the number of wafers sampled per lot from one to six, but the benefit diminishes beyond five~ten wafers/lot. Sampling plans with nine fields per wafer and six fields per wafer are also compared. The achievable benefit of sampling more wafers is smaller if the withinwafer sample is insufficient. It implies that sampling more fields is more beneficial than sampling more wafers, given the same metrology resources.

F i g u re 4. Overla y residual ba seli ne tre n d .

errors for one wafer, three wafers and five wafers, each with 16 random combinations of wafers are calculated.

Then the maximum and minimum worst prediction errors for each number of wafers are compared, as shown in Figure 5. In this case, randomly sampling five wafers within the lot significantly reduces the overlay correction error.

To assess the wafer sampling needs for the second objective, multiple baseline lots of sufficient number of wafers need to be characterized. Unfortunately, this is not available from the historical overlay data, as only one wafer per lot is measured in the production sample plan. To illustrate the need, we show the result of CD sampling optimization from the same layer and product. Similar analysis can be done for overlay when the data becomes available.

In Figure 6 we analyze the opportunity for reducing material at risk with increased wafer-to-wafer sampling. The risk reduction of sampling more wafers compared to one-wafer-per-lot sample is shown.

Significant risk reduction can be achieved by increasing

F i g u re 5. Wa f e r-to-wafer s ampling o ptimization f or mini mizing overl ay c o rrection erro r s .

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F i g u r e 6. Wa f e r-to-wafer and within-waf er sampling optimization for min imiz ing lo ts at ri sk.

Within-wafer sampling Within-wafer sampling concerns primarily the need for selecting proper number and locations of fields to minimize correction errors. The “overlay field selector (OFS)” approach as described in2 is used to assess this need. Three lots of wafers from several layers are measured at every field. This provides a complete characterization of the spatial signatures of the layers, as shown in Figure 7. We have observed signatures such as “radial,” “spiral,” and “localized” that reflect the prior CMP or thermal process effects.

In Figure 8, we show the OFS results of prediction errors for various sampling plans for M1 and contact layers. The range of prediction errors for each number of fields is also indicated on the charts. For layers with different baseline signatures, the prediction error convergence rates with respect to number of fields sampled are different; therefore, the optimal numbers and spatial locations of fields are different.

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(2) Reducing transport time through automation (3) Increasing bottleneck tool capacity (case 2: Four CD SEMs versus three CD SEMs) Figure 10 shows the cycle time estimated for three alternative metrology combinations and the associated percentage of material at risk due to overlay excursions. The baseline using three CD SEM and three overlay tools has a mean litho cycle time of three~four hours, which exposes 1.03 percent of the lots to undetected excursions.

The tool combinations in cases two and three significantly reduce the cycle time. Reducing cycle time reduces lots exposed to overlay excursions.

Consequently, the lots at risk due to overlay excursions are more than half reduced with cases two and three.

F i g u re 7. Within-wa fer sampl ing: baseline sign ature char acterization .

F i g u re 8. With in-wafer sa mpling optimization fo r mi nimizin g c orrection erro r s .

Cycle time effects From the historical database, we extracted the time stamps at each litho processing or metrology step. The processing sequence in the case study is: litho -> CD SEM -> Overlay. Figure 9 shows the litho cycle time and its components characterized for the baseline lots.

In this case, transport and CD queue contribute the most to litho cycle time and its variability. The high queuing time at CD SEM is due to insufficient CD capacity and relatively high CD measurement time.

With the baseline case representing the fab current situation, we explored several options for reducing cycle time, and evaluated the impacts of cycle time on lots exposed to excursions.

With fixed sampling plans, cycle time can be reduced in the following ways: (1) Alternative tool configurations, such as using optical CD instead of CD SEM, which has faster processing time (case 3: Three spectroscopic CD tools and three overlay tools)

F i g u re 9. Baseline litho mod ule cycle ti me comp onen ts.

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F i g u re 10. Reduce excursion risk through cycle time re d u c t i o n .

Summary

We developed a comprehensive overlay excursion management methodology that encompasses excursion separation, baseline variance estimation, cycle time analysis, and sampling plan optimization for minimizing material at risk. Systematic and random variance components decomposition of overlay should take into account the spatial correction model and the uncertainty in estimating parameters. From actual 300-mm fab overlay data analysis, we showed that major opportunities for variance reduction include within-wafer control, and queue management at metrology. We also conclude from production data that wafer-to-wafer variance is not significant compared to lot-to-lot and within-wafer variances. However sufficient wafer-to-wafer (three~six) and within-wafer sampling are required to reduce material at risk. Furthermore, we demonstrated that cycle time has a significant impact on lots exposed to

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excursion risks, and can be reduced through alternative metrology configurations.

References 1. R. Elliott, R.K. Nurani, et al., “Sampling Plan Optimization for D et ect io n of L ithog ra phy and E tch C D Pr o ce s s Excursions”, Metrology, Inspection, and Process Control for M i c rolithography XIV, Proceedings of SPIE Vol. 3998, pp. 527-536, 2000.

2. X. Chen, M. Preil, M. Goeff-Dussable, M. Maenhoudt, “An Automated Method for Overlay Sample Plan Optimization Based on Spatial Variation Modeling”, P roceedings of SPIE Vol. 4344, pp. 257-266, 2001.

A version of this article originally published in the 2003 SPIE Microlithography proceedings 5038, SPIE Micro l i t h ography Conference, February 2003, Santa Clara, California, USA.