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Increasing Learning Rate On Copper Processes

by Jeff Lin, Yield Enhancement Engineer, Motorola, APRDL

In the yield-learning phase of a new process, such as copper dual damascene, one of the challenges is efficient defect detection. A novel statistical method for optimization of copper inspections and sampling strategies was introduced which facilitated increased yield learning rates on the copper process at Motorola APRDL. KLA-Tencor 2138 inspections on copper processes were initially optimized for maximum sensitivity to all defect types. As understanding of copper grew, the tendency for non-killer defects to outnumber killer defects became evident. Because a random sample of defects was sent on for further review, the percent killer defects in the sample was important. Looking beyond the standard methods for increasing the percent killer defects captured in the sample was one of the keys to bringing the copper process to yield quickly.

Killer defects are those most likely to compromise the functionality of the device.

They include large particles, bridging defects, missing patterns, residues, corro-

sion, and defects of unknown origin or composition. Non-killer defects are those less likely to affect a device, such as small defects, small particles, polish slurry, color variation, and other nuisance defects.

Examples of typical killer and non-killer defects for copper processes are shown in figures 1 and 2.

The yield enhancement tool set included a KLA-Tencor 2138 inspection system with IMPACT/Online ADC. Data was downloaded and analyzed using the Klarity analysis system.

Methodology

There are a lot of methods available to reduce the capture rate of non-killer defects on the 2138, including raising the sensitivity threshold, filtering out smaller defects, and using a larger pixel size to lower the system’s resolution. Image smoothing through filters could reduce the non-killer defect count, as could using a tuned segmented auto-threshold (SAT) inspection. ADC could be used to sort nuisance defects; then the sample could be

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F i g u re 1. Killer def ect exampl es fr om Motorola APRDL’s copper process. In p icture 1a,

F i g u re 2 . Non-kill er defect examples from Motor o l a

a la rge particl e has shorted s evera l m etal l ines. Picture 1b shows a scratch. Picture s

A P R D L’s copper process. Pictur e 2a sh ows s lurry

1c an d 1f are exa mples of residue defe cts. Picture 1d shows a section of missing

res idue. Picture 2 c shows a small parti cle out in the

metal. Picture 1e shows brid gin g of a few metal lines.

fi eld area. Pictures 2 b and 2d are color variation .

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F Parameter Set #

Relative Sensitivity

Settings

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Size Sieve

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high

SAT, not filtered, low threshold

no

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medium/high

SAT, filtered, low threshold

no

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medium

SAT, not filtered, high threshold

no

4

medium/low

Fixed, medium threshold

yes

5

low

Fixed, high threshold

no

Table 1. Details of the five parameter sets used to optimize for killer ratios.

taken from the non-nuisance bins.

Motorola APRDL decided to try a novel statistical approach to determine which method or combination of methods was optimal, taking the sample plan and inspection time into consideration.

Four metrics were used to evaluate the effectiveness of an inspection: • The percent killer defects detected, or killer sensitivity, measures the percentage of killer defects detected by the inspector, referenced to the number of killer defects known to be on the wafer. For example, if a 2138 scan found 40 killer defects on a wafer with 50 known killers, the killer sensitivity would be 80 percent. The number of known killers is determined by scanning the wafer using a significantly more sensitive inspection technology.

Motorola APRDL chose to use the SEMSpec as the standard.

used for comparison to the 2138 inspection data collected later in the experiment. A SEMSpec was chosen as the reference inspection technology because its scanning electron microscope-based technology provides significantly more sensitivity than that provided by the optical-based 2138.

F i g u re 3A. Metal 2: killer/non-kill er d efects by work week. Green bars re p resent normalized def ect counts o f the samp led non -killers, while orange bars are the kill ers.

The improvement in s ampl ing of killers is si gnificant aft er the new method was imple-

• The killer-to-total ratio, or signal-to-noise ratio, calculates the ratio of killers found to the total number of defects detected by the inspector. For example, if the 2138 found that 40 defects out of a total of 100 were killers, the signal-to-noise ratio would be 40 percent.

• The killer normalized defect density extrapolates the killer defect density of the sample over the entire population of defects found on the wafer.

• The fourth measurement is the inspection time, including wafer handling, alignment, scanning and ADC image collection time.

The first step was to collect reference data to determine the number of “known” killer defects on the wafer. This data set would be

mented on the Metal 2 wafer.

Thus, a SEMSpec was used to scan a Metal 2 copper wafer and determine how many detected defects were killers. The assumption was that the SEMSpec caught all the killers that the 2138 would have been able to find, and more; any defects unique to the 2138 scan were assumed non-killer.

The next step involved setting up multiple inspections on the 2138, with varying sensitivity parameters. Five sets of scans were performed each day with four different pixel sizes, for a total of 20 scans. Table 1 shows the details of the five recipe settings.

Under the assumption that a large defect has a higher probability of being a killer defect, the goal was to quantify the tradeAutumn 1999

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F i g u re 3B. Metal 2: percen t killers in sample. Th e i m p rovement i n sampling of killers after th e new method is introduced is more evident when per c e n t kill ers i n the samp le is plotted for the Meta l 2 w a f e r.

off between reduced scan time and sensitivity so that the largest pixel size (shortest scan time) providing adequate defect capture could be used. In setting up the 2138 inspections, the origin marks were chosen to duplicate the origin marks on the SEMSpec scan. Inspection comparison percentages were calculated by overlaying the 2138 data on the SEMSpec data using the Defect Source Analysis algorithm on Klarity.

Results from a comparison of the five scan types, for each of the four pixel sizes, emphasized that optimizing a 2138 for high sensitivity to all defects — the old method — does not provide the highest ratio of killer to nuisance defects (signalto-noise ratio). A compromise in overall sensitivity can create a detected defect population that more accurately reflects the entire killer defect population on the wafer. After the 20 scans indicated which inspection was best suited to inspection of the Metal 2 wafer, the scan recipe was refined further using standard recipe optimization procedures, to improve killer ratio and capture rate.

Results

F i g u re 4A. Metal 3: killer/non-killer defects by work week. Green b ars re p r esent no rm a l i z e d defect counts of the sampl ed non-killers , while orange ba rs are th e kil lers. The impr ovement in sampling of killers is evident for Meta l 3, alth ough not as large as for the Metal 2 wafer.

Figure 3A shows the improvement in percent killers found within the defect sample after implementation of the method described above, for a Cu Metal 2 layer.

Generated from the Klarity pareto charts, this chart uses green bars to represent normalized defect counts of the sampled nonkiller defects, while the orange bars represent normalized defect counts of sampled killers. The bias towards sampling of nonkillers is evident in the data taken before the new methodology was applied. After the new method was implemented, a higher percentage of killers was detected.

Figure 3B shows the data in terms of percent killers found within the sample plan.

The improvement in percent killers sampled is even more visible.

F i g u re 4B. Metal 3: percent kil lers in sa mple. The i m p rovement in sampling of kil lers after the new method is introduced is sm all but significa nt f or the Metal 3 wafer.

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When this method was applied to other layers, improvement in percent killers detected was demonstrated further. Metal 3 zone data, shown in figure 4A, shows a clear rise in sampling of killer types after implementation of the new method. In

terms of percent killers found within a sample (figure 4B), the change for Metal 3 was not as noticeable as with Metal 2; the sampling of killers and non-killers tended to be more consistent. This can probably be attributed to the higher probability of detecting defects from previous levels at higher metal levels.

Results from the Metal 5 zone again show some improvement after implementation of the new method (figure 5A). For Metal 5 the change in percent killers is more noticeable than for the Metal 3 zone (figure 5B).

Summar

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F i g u re 5A. Metal 5: killer /non-killer d efects by work week.

Green b ars re p resent no rm a l i z e d

defect count s of the s ampl ed non-killers, while orange bars are th e killers. Th e improvement in samp lin g of killers is clearly demonstrated for

At Motorola APRDL, a new methodology for increasing capture and sampling of killer defects has been developed and tested. First, a reference data set was determined; Motorola APRDL chose to use a SEMSpec scan as the reference. Then multiple inspection setups were created for the 2138, with various sensitivities, and various wafers were scanned using these parameters. The 2138 data was overlaid to the SEMSpec data by using the Defect Source Analysis algorithm on Klarity, with a userdefined tolerance radius. Killer sensitivity and signal-to-noise ratios were generated.

After the best inspection recipe was chosen it was refined further to improve killer ratio and capture rate.

This study demonstrated that using a statistical approach allows the user to choose optimal inspection parameters for increased sensitivity to killer defects. In particular, Motorola APRDL found that implementation of this method increased the yield learning rate on new copper processes. Wafer review and data analysis became more productive, and killer defect density paretos became more accurate and understandable — especially valuable improvements in an R&D environment such as APRDL. ❈

Meta l 5.

F i g u re 5B. Metal 5: percent killers in sa mple. The i m p rovement in sampling of killers after the new method is introduced is demonstrated for the Meta l 5 waf er.

cir cle RS#039

This paper was first presented at KLA-Tencor’s Yield Management Solutions Seminar at SEMICON/West in July 1999. It was edited for this publication by Rebecca Howland Pinto, Ph.D., in KLA-Tencor’s WIN Division.

F i g u re 6. I nspection Optm ization Methodol ogy

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