Magazine spring06 when raise red flag

Lithography R

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When to Raise the Red Flag Effective Dispositioning of Defective Masks Jerry Huang, Lan-Hsin Peng, and Chih-Wei Chu, ProMOS Technologies Kaustuve Bhattacharyya, Ben Eynon, Farzin Mirzaagha, Tony Dibiase, Kong Son, Jackie Cheng, Ellison Chen, and Den Wang, KLA-Tencor Corporation

Progressive mask defects are an industry-wide mask reliability problem, particularly when the defects approach the critical state where the mask either needs to be pulled out of production or sent for cleaning (or repair). This problem is especially troublesome with expensive high-end masks running deep ultraviolet (DUV) lithography. In these cases, the fab will want to sustain the problematic masks in production as long as possible, until just before the masks begin impacting the process window. This study found that while a small, growing defect may not print at the best focus exposure condition, it can still influence the process window, shrinking it significantly. Direct, high resolution reticle inspection enables early detection of these defects; however, fabs still need an effective means to disposition defective masks. A lithographic detector has been evaluated to see if it can predict the criticality of such progressive mask defects.

Examining the nature of mask defect growth

In a typical fab, many masks remain problem-free (clean) even after a large number of exposures. On average, about 1% of binary masks (at 365 nm lithography) and 6% to 15% of embedded phase shift masks (EPSMs) (using DUV lithography) show a defect growth problem through the duration of their usage in the fabs1,2. A direct, high resolution mask inspection can detect these defective masks effectively. But the nature of this defect growth can be severe on some masks, which means thousands of real crystal growth-type defects on the pattern side of masks. As one can imagine, this can

Figure 1. Progressive mask defects growth in fab.

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render the defect review session of these problematic masks to be quite complicated. However, a mask inspection tool such as KLA-Tencor’s TeraScan STARlight has the capability of binning the defects by size as well as by type (such as “on-chrome”, “on-clear”, “on-half-tone”, etc.). This helps to disposition masks that have a reasonable number of defects. When the total defect count grows on certain masks, however, traditional review techniques may result in a very lengthy review session.

Hence, it was decided that the new TeraScan STARlight-2 (SL2) should be used to evaluate a run-time mask error enhancement factor (MEEF) based detector that may isolate the defects of interest from those thousands of total defects caught by SL2.

In Figure 1 below, a fab’s incoming inspection indicates that a mask arrived clean, but after only 20 days of

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production usage, the mask showed catastrophic defect growth. While defects on the mask image to the left can be easily reviewed individually (manually), the total count of the defects on the mask image to the right is so large that a manual review is difficult. A more automated way to review defects is needed.

Impact on the process window

Understanding the impact of small progressive defects on the process window is absolutely critical. Some critical mask defects will print on the wafer (collapse process window), and some will have no impact at all. But there will be many defects on these highly defective masks that will fall in between these parameters. They may not completely collapse the process window, but may reduce it. Criticality of mask defects has to be examined from this point-of-view, as any reduction in process window can potentially cause problems.

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a big role. If the defect location and size are kept the same and the half pitch of the mask is reduced, the probability of the defect to cause bridging on the wafers will increase. This means a mask defect (in Figure 2, consider the 120 nm defect) that caused no problem in the past on older design nodes (like at the 135 halfpitch process) may now impact the process window on a new design node (90 nm half-pitch) due to denser geometry and higher MEEF. When the defect size becomes 200 nm, it completely collapses the process window.

Simulation Results at 193 nm Exposure and 0.75 NA

Figures 3 and 4 show the process window effect of a 120 nm defect on mask. With the tighter design rules, the volume of defects of concern continues to increase.

Focus

Exposure

It is also important to not only consider a defect from pure size, as contamination defects are not completely opaque and can be considered phase defects. A combination of defect size, transmission loss, and location should provide a good indication of the defect’s criticality. This makes MEEF-based binning necessary.

It can be seen from the simulation images in Figure 2 that the half pitch of the mask and defect location plays

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Figure 3. A 120 nm mask defect did not impact the Focus process window for 135 nm half-pitch process.

Focus

Defect size (on mask)

K1 factor

135nm

120nm

0.52

135nm

200nm

0.52

135nm

320nm

0.52

90nm

120nm

0.35

Wafer image

Exposure

Mask defect

Exposure

Half pitch

Figure 4. A 120 nm mask defect reduced the process window significantly at 90 nm half-pitch process.

90nm

200nm

0.35

It can be seen that if the mask defect increases in size to 200 nm, there will be no process window left in the figure below for this 90 nm half-pitch process.

Figure 2. Mask defects being simulated on wafer — MEEF and defect size impact.

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NM NODE %03- IS PRINTED USING A DOSE MATRIX

Focus

n

-ASK $EFECT $EFECT 0RINTED ON 7AFER

Exposure

"EST %XPOSURE #ONDITION .OMINAL DOSE

Figure 7. Mask defect impacting the process window — wafer images from CD Figure 5. A 200 nm mask defect collapses the process window at 90 nm

Developing the detector

half-pitch process.

Printed mask defect and the process window

A 110-nm node 193 EPSM was inspected and then exposed using a dose matrix. Dose was varied +/- 20%.

The results are as follows: -ASK

SEM tool.

-ASK $EFECT

$EFECT 0RINTED ON 7AFER

Litho3 and ReviewSmart on KLA-Tencor’s TeraScan mask inspection tool are the two detectors being characterized. This paper provides preliminary results from a few inspections performed with the new TeraScan STARlight-2 (SL2). A complete characterization was beyond the scope of this work. Further characterizations will be required in the near future to understand the implication of these detectors on various mask layers and nodes.

Litho3

A combination of defect size, transmission loss, and location should give a good indication of the defect’s criticality. So a MEEF-driven litho type detector was developed to use at run-time. With the proper setup, this detector is capable of binning critical defects under a single bin. This detector uses the following concepts: a. It contains a group of specialized detectors that operate run-time based on geometry and lithographic context

Figure 6. Mask defect printed on wafer.

A dose matrix exposure of this mask showed that even a small defect can impact the process window. The defect was visible at slight under-dose conditions. Even -5% dose variation from the best exposure condition enhanced the printability of the defect.

Substantial ongoing investigation is required to understand the printability of contamination defects. However, in this current work, the focus was kept on the development of an effective disposition method for mask defects (go / no-go criteria). For simplicity, all of the work here is based on an optical image from the mask (via the TeraScan inspection system), with the goal being to develop a run-time detector to provide speed.

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b. Defects of the same size and intensity will be binned differently based on the defect location MEEF; i.e., a certain defect located in a high MEEF area will go in a particular litho bin while a similar defect (in size and intensity) that is in a low MEEF area will remain excluded from this special bin.

If the litho detector is set up correctly, it will bin all the critical defects of interest in the special bin. The thresholds that control this bin are user definable from the setup. An initial evaluation showed encouraging results.

The mask below had roughly 700 defects. When the litho detector was used, some of these defects were binned in a special bin (run-time). All of the defects in

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!LL CRITICAL DEFECTS ARE HIGHLIGHTED IN RED

.ON CRITICAL DEFECTS ARE HIGHLIGHTED IN YELLOW OR GREEN Figure 8. Litho3 detector binning the critical defects (in red) on a TeraScan SL2 system.

this special bin were critical in nature and can be seen below. These are all highlighted in red for visibility.

Non-critical defects are shaded in yellow and green. The user can tighten these special bin criteria so that fewer defects are shaded in red. But care must be taken to set these criteria correctly.

ReviewSmart

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The ReviewSmart detector is developed based on the goal of more effective binning of similar defects3.

ReviewSmart identifies defects which are lithographically similar using a set of operator-specified thresholds.

These similar defects are then binned into a group. A large number of defects can be binned into only a few groups and within each group, defects are also ranked by severity. All of these happen at run-time from the main GUI (no extra simulations to run). The operator then just needs to classify one defect from each of these groups, rather than reviewing all defects and having the rest of the defects auto-classified. The following is an example from a highly defective mask where ReviewSmart was able to bin small crystal growth-type defects on isolated (non-critical) areas effectively, placing this type of defects in a single bin while keeping defects on dense geometry in a separate bin.

Conclusions

Progressive mask defects such as crystal growth and haze continue to threaten the industry. Resolution requirements have driven the IC industry to implement very low k1 lithography processes, which elevate the impact of mask errors as a result. Simulation data and then a real print-study on a 193 EPSM showed that some of the critical mask defects will print on wafer (collapsing process window) and some will have no impact at all. But there will be many defects on these highly defective masks that will fall in between these, impacting the process window if they print at slightly under-dosed conditions. Such defects may not completely collapse the process window, but they will reduce it. Criticality of mask defects has to be examined from this point-of-view, as any process window reduction can potentially cause a problem.

A certain percentage of masks (1% to 15%) show this progressive defect problem. High-resolution mask inspection will detect this defect problem. But initially, many of these defects are just forming and not so opaque in nature. During defect review on a mask inspection tool, sorting out critical defects from this ocean of nascent defects is no simple task. Disposition of such mask defects becomes a lengthy task when a large number of defects (mainly progressive

Figure 9. ReviewSmart binning of defects on TeraScan systems.

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defects) are present on the mask. Effective disposition of a highly defective mask needs carefully set, productionworthy go / no-go criteria.

The new lithographic run-time detectors developed and tested during this work targeted only contamination defects. Some initial characterization will be required to fine-tune this detector in the fab. This study showed promise towards creating a helpful tool for mask disposition in the fabs. Efforts will continue over the coming months to perfect these detectors and their usage.

Acknowledgements

The authors would like to thank the following individuals for their contribution: William Volk, Qiang Li, Steven Labovitz, Ching Yun Hsiang, Paul Yu, Amir Azordegan, and Zhian Guo of KLA-Tencor Corp.

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and Den Wang, Mask Defect Dispositioning, in 25th Annual BACUS Symposium on Photomask Technology, edited by J. Tracy Weed, Patrick M. Martin, Proc. of SPIE Vol. 5992, 59921X, (2005) CID# 599206 References 1. K. Bhattacharyya, M. Eickhoff, Mark Ma, Sylvia Pas, A Reticle Quality Management Strategy in Wafer Fabs Addressing Progressive Mask Defect Growth Problem at low k1 Lithography, Photomask Japan, 2005 2. K. Bhattacharyya, K. Son, B. Eynon, D. Gudmundsson, C. Jaehnert, D. Uhlig, A Reticle Quality Management Strategy in Wafer Fabs Addressing Progressive Mask Defect Growth Problem at low k1 Lithography, BACUS Symposium on Photomask Technology, 2004 3. P. Yu, V. Hsu, E. Chen, R. Lai, K. Son, W. Ma, P. Chang, J. Chen, Implementation of an Efficient Defect Classification Methodology for Advanced Reticle Inspection, Photomask Japan, 2005

Jerry Huang, Lan-Hsin Peng, and Chih-Wei Chu, Kaustuve Bhattacharyya, Ben Eynon, Farzin Mirzaagha, Tony Dibiase, Kong Son, Jackie Cheng, Ellison Chen,

2006

KLA-Tencor Trade Show Calendar February 21-22

SPIE Microlithography, San Jose, California

March 6-7

IC China, Shanghai, China

March 21-23

SEMICON China, Shanghai, China

June 6

IITC Hospitality Reception, Burlingame, California

July 12-14

SEMICON West, San Francisco, California

July 12

YMS West, San Francisco, California

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