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Real-Time Classification Streamlines Yield Management Process

by Rebecca Howland Pinto, Ph.D., Director of Marketing, WIN Division

The introduction of automatic defect classification (ADC)1 may have been the most important advance in yield management in recent years. When ADC was first introduced on off-line review stations, customers indicated that ADC provided more reproducible results more quickly and less expensively than using technicians to classify defects. When ADC software and hardware were moved on to the inspection platform itself, utilizing the integrated optical review microscope, the time to classification was improved again — the cassette of wafers did not have to be transferred across the bay to a separate review microscope, where it might wait in the queue for an hour or more.

KLA-Tencor is now refining the concept of automatic defect classification even farther.

With the ongoing goal to reduce the time required to classify all defects on the wafer, KLA-Tencor has put in place the ability to make a first-pass classification as the wafer is being inspected. Those defects sorted into the nuisance class require no further investigation. They can be removed from the process control charts, cleaning up and purifying the signature of process excursions.

Defects in the killer classes may be passed on to the high-resolution ADC subsystem described above — if high-resolution classification is even necessary. During the highresolution ADC step, the sampling strategy can now be more intelligent: instead of choosing a manageable number of defects randomly from an unsorted defect set, the samples can be chosen from the defects of interest. Potentially all of the defects of interest can now be classified in a reasonable amount of time. Finally, a small number of defects may pass to SEM review after high resolution ADC. Only at this last step do the wafers leave the inspection platform.

The first step of the streamlined process is called real-time classification (RTC). Real-time classification utilizes data collected during the inspection by the inspector’s sensors and 10

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optics to extract descriptors used to separate the defects into coarse classes. Because the defects detected do not need to be re-detected and re-imaged, RTC maintains the throughput entitlement of the inspection system. In contrast, high resolution ADC (HRDC) utilizes data collected after inspection from the inspector’s built-in review microscope (or an off-line review station).

Characteristics from the high-resolution images used for HRDC are used to separate defects into more specific classes. Figure 1 shows the dramatic time savings accomplished by using this multi-step process.

F i g u re 1. Real-Time Classif ication is part of the ADC pat hway, re d u cing th e number of d efects requirin g HRDC. Th e fi rst four actions t ake place with no disc ernible effect o n thro u g h p u t .

provide accurate classification of most defects into preliminary classes that are acceptably pure. (See definitions of accuracy and purity in the sidebar.) In fact, an ideal RTC package would be as close as possible to HRDC in performance. Furthermore, this performance should be achieved without noticeable impact on the inspector throughput.

F i g u re 2. RTC can en abl e sensitivity to be i ncreased at t he inspector recipe level in some cases, because false/nuisance d efects are kep t under contro l .

How RTC provides value

As described above, real-time classification enables a more intelligent sampling strategy for high-resolution defect classification. RTC also provides value by immediately removing nuisance defects — defects not of interest because they are believed to have no effect on yield.

Once RTC separates defects into preliminary classes, statistical process control (SPC) algorithms can be used to monitor defect excursions by type. Monitoring defect count for each important yield-limiting defect type has been shown to be much more effective than monitoring total defect count. 2 Excursions of a particular defect type can be lost completely when only total defect count is trended. Such trending can help detect “hidden excursions” and provide a basis for baseline reduction efforts.

Another way RTC provides value is by maintaining the sensitivity entitlement of the inspection system.

“Hotter” recipes are possible when nuisance events are eliminated during scan time. In the absence of RTC, a fab engineer often reduces the sensitivity of a recipe so that it captures most of the defects of interest while at the same time limiting capture of nuisance defects.

Because RTC can be very effective at screening out nuisance defects, RTC may allow the engineer to increase the sensitivity of a recipe to capture more defects of interest (figure 2).

Components of a good RTC package

Several capabilities are considered crucial in the design of a leading-edge real-time classification package. First, performance is paramount — an RTC package should

An ideal RTC package should be easy to set up and use.

The number of classes into which defects can be sorted should be flexible (while maintaining acceptable computation time) and should be customizable by recipe.

The ability to specify classes according to process layer is necessary to achieve high performance across different layers. For example, users can separate “color” as a nuisance defect on a CMP layer while independently separating “grain” as a nuisance defect on a metal layer.

Characterization studies have shown that classificaOutputs of RTC — tion based on pre-set Accuracy and Purity classes common for all As with HRDC, the success of layers does not provide the RTC on a given defect set is desired yield management measured by the accuracy and performance. With the purity of the results. The defi ongoing introduction of nitions used for RTC are new processes and materitaken from definitions intro als in IC manufacturing, duced by HRDC. For a given the ability to customize class: the classification system • Accuracy is the ratio of the for new defect types is number of defects classified critical.

The relationship of RTC to the rest of the ADC pathway should be well designed. In RTC, the defect signal during inspection is parameterized and used for sorting; this information is of further use if the defect is classified later by HRDC. For this reason, a “feed forward” information pathway is part of the design of an efficient RTC-HRDC process. By the same token, information provided by HRDC can be used during RTC setup. A “feed back” information pathway is also part of an effective ADC process.

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correctly by RTC (where cor rectly means in agreement with a human expert) to the number of defects put into the class by the human expert. In other words, accuracy mea sures agreement between the RTC system and a human expert.

• Purity is the ratio of the number of defects classified correctly by RTC (same numerator as accuracy) to the number of defects put into the class by the RTC system. In other words, purity indicates the number of correct calls made by RTC as a percent of defects sorted into a given class.

Figure 3 illustrates the con cepts of accuracy and purity for a simple case.

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Defects clas sified as residues by RTC but found to be scra tches b y human ex pert

Defect s class ified as res idues by human exp ert

Defects classified as resi dues by RTC a nd human expert

Defects cl assified as resid ues

by RT C

Accuracy = 70/100 = 70% Puri ty = 70/80 = 82. 5%

F i g u re 3. Definitions of accuracy and purity.

Finally, inspector sensitivity is of critical importance in any kind of defect classification. You cannot classify defects that are not detected. In essence, real-time classification is only as good as the quality of the information from the inspector. RTC cannot be used to compensate for a fundamental lack of sensitivity to defect types.

The issue of inspector sensitivity shows up in the signal-to-noise ratio of the defect. Defects too small or too low in contrast to be detected with strong signalto-noise by an optical inspection system will not be classified well using optical RTC or optical HRDC. These defects may be detected better with a SEMbased inspection system such as KLA-Tencor’s eS20, and would likewise be classified more accurately using SEM ADC, as found on KLA-Tencor’s 4300+.

In accordance with the strategy described above, KLA-Tencor has designed RTC packages for darkfield (AIT) and brightfield (2xxx) product lines. These products not only provide highest performance, but also minimize impact on inspection time, with straightforward user interfaces, configurable classes, automatic figure of merit generation, and the ability to exchange information with HRDC. Examples of results from these systems are described in the following section.

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R TC on brightfield and darkfield inspectors

Recall that the main difference between RTC and HRDC is that, while HRDC uses higher resolution information gained after the inspection from an automated, often integrated, review microscope, RTC Real-time equals utilizes only the informar eal value tion collected during an inspection. Thus HRDC In today’s market products images would be similar that claim to do real-time on a brightfield inspector defect classification go by (like a 2xxx) and a darkmany names. In-line, onfield inspector (like an the-fly, run-time and noAIT), since both utilize overhead are phrases that brightfield optical review often comprise the first part microscopes. However, it of the term. The phrases are is important to note that meant to convey that the the defect signals captured classification process has lit on a brightfield inspectle or no impact on inspector tion system are qualitathroughput. There are two tively and quantitatively ways in which a real-time different from the signals classification process can add captured on a darkfield literally no time to the system. Therefore RTC inspection: employing a sepa on a brightfield system is rate, parallel microprocessor working with substanor utilizing idle time for the tially different informasingle microprocessor, perhaps tion from RTC on a darkwhen a mechanical motion is field system. taking place in the absence of The defect signals generated by a brightfield system are very similar to images captured by optical review microscopes — which are generally used in brightfield mode. The difference is mostly one of resolution.

KLA-Tencor’s darkfield inspection systems are designed for high throughput to meet the low cost of ownership requirements for process tool monitoring.

Parameterization of the defect signals from these tools is inherently less

data collection. Especially because inspector throughput has received so much atten tion in today’s equipment design, such idle time can be difficult to find. Efficient algorithms are essential to RTC. Utilizing fixed defect classes and descriptors would be an additional time saver, but its price is high. Defect classification would not be as accurate and pure on all layers; correlation to yield killers specific to a process layer would not be as high, and performance of the yield management system would suffer. KLA-Tencor elected to forego fixed classes in favor of higher performance with its RTC solution.

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S u rf a c e Layer Ripout

S u rf a c e P a rt i c l e

Residual S l u rr y

P re v i o u s Layer µscratch

P revious Layer µscratch ( A rr a y )

S u rf a c eP re v i o u s

P re v i o u s L ayer Part i c l e

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F i g u re 4. Curren t layer and previous layer defects on an oxid e CMP l a y e r, for a 0.25 µm design r ule device.

complex. The defect attributes are used to group and separate nuisance defects from defects of interest while also allowing sorting into multiple, user-configurable classes.

Recent results using RTC

Case 1: Using RTC as an effective previous-layer defect filter on the AIT platform A simple but highly effective use of RTC came to light recently when a customer of KLA-Tencor wanted to separate current-layer defects from previous-layer defects on an oxide CMP layer, for a device having a 0.25 µm design rule. The customer was interested in only the current-layer defects, including microscratches, slurry and rip-out defects (figure 4). The difficulty was that the defects of interest were in the minority; the transparent TEOS film was allowing numerous previous layer defects to be detected. The AIT II’s optical design helped to minimize capture of previous layer defects: oblique incidence minimizes penetration of the beam below the surface (since reflectance increases at oblique angles), and oblique detection minimizes collection of the sub-surface defects that do manage to scatter significant light. Even so, a large number of previous-layer defects were showing up on the wafer map.

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and purity of the surface-layer class was 79 percent.

Most importantly, RTC enabled the AIT II to run a “hot” recipe to capture the small population of surface defects within the large population of previous-layer defects. Previous to RTC, less than 3 percent of the total defects on the wafer map were of interest; after RTC, 95 percent of the defects identified as surfacelayer were key to the subsequent investigation into the source of the defects.

Case 2: Using RTC to classify defects that touch metal lines on the AIT platform A less simplistic use of RTC on the AIT platform was shown when RTC was used to identify defects that touch metal lines on a Metal 6 post-etch inspection.

The AIT II detected 1722 defects. The object of the RTC exercise was twofold: first to remove nuisance defects arising from metal grain, and second to separate the defects touching metal lines from other non-nuisance defects. After the metal-grain nuisance defects were removed, the remaining defects that touched metal lines were discovered to group together on a two-dimensional feature plot (figure 6).

Defining the non-nuisance classes using this methodology resulted in very successful separation of the defects of interest. After 55 metal-grain nuisance defects were removed from consideration, RTC classified 51 of the remaining 1667 defects as touching metal lines. When these defects were reviewed, accuracy for this class was 78 percent and purity was 84 percent.

RTC was used simply to divide the defects into two classes using these attributes, as shown in figure 5.

This very fast and simple version of RTC was nonetheless extremely effective.

More than 3000 defects were captured on the wafer by the AIT II, with RTC identifying 69 as surface-layer.

Manual review by an expert identified 73 as surface layer and the rest as previous-layer. Thus, accuracy on identification of surface-layer defects was 95 percent,

F i g u re 5. RTC on the AIT platform successfully separated pr e v i o u s la yer (nuisance) from current-layer defects. Before RTC, less th an 3 percen t of d efects were of interest (73 curr ent layer out of 3000 total). After RTC, 95 percen t of d efects are of interest — RT C class ified 6 9 of 73 corre c t l y.

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F i g u re 6. Sep arat ing defects t hat touch metal lines on a M6 post-etch layer using RTC on the AIT platfo rm .

Case 3: Using RTC to find missing metal defects on the 2xxx platform RTC was the answer in another situation where the defects of interest were in the minority. A Metal 1 wafer inspected by the 2138 system was found to have 2671 defects — mostly nuisance defects arising from metal corrosion. In this case the fab was looking for “Missing Metal” defects, such as the one shown in figure 7.

Using 94 defects to train the RTC system, RTC employed 3 of a possible 36 descriptors to identify 972 defects as missing metal defects. When 771 of these defects were revisited for manual classification, it was found that RTC had correctly classified 763 of these defects, for an accuracy of over 98 percent.

F i g u re 7. Example of miss ing met al defect type wh ose population wa s over whelmed by n uisa nce, corrosion defect s. RTC on th e 2xxx p l a t f o rm found 9 72 of these d efects among over 2600 total defects.

What happens next?

The utility of RTC to extract defects of interest, permit more sensitive recipes on the inspection system, enable a more efficient sampling strategy for HRDC and reduce the number of defects that need manual classification is making RTC an object of much interest in leading-edge fabs. What’s next for RTC? The most likely extension is on the SEM inspection platform, as SEM inspection tools become more and more necessary for capturing small killer defects on devices with design rules of 0.15 µm or less. RTC capability is also predicted to expand as optical inspection systems continue to evolve — and as parallel processing becomes increasingly practical. ❈ cir cle RS#015

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1. For further reading on ADC: Breaux, L., Kolar, D., “Automatic Defect Classification for Effective Yield Management”, Solid State Technology, Vol. 39 No. 12, pp. 89-96 (1996).

2. See for example “Applications and Benefits of AIT/ADC Back-End-of-theLine Process Development and Tool Monitoring”, John Alvis, Andy Campbell, Sean Collins, Nancy Benavides, Michael Peterson (Motorola APRDL), David Price and Frank Fan (KLA-Tencor), Yield Management Seminar, SEMICON/ Southwest, October 1998. Another example is “Tracking the Performance of Photolithographic Processes with Excursion Monitoring,” Eric H. Bokelberg and Michael E. Pariseau (IBM Microelectronics), MICRO, Vol 16, No. 1, pp. 47-58 (1998).