Magazine spring01 defectplaning 300fabs

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Defect Sample Planning in 300 mm Fabs

by Dadi Gudmundsson, Natraj Narayanswami, Raman Nurani, Ph.D., Anantha Sethuraman, Ph.D., Mark Shirey, KLA-Tencor Corporation

The move to a smaller design rule and the associated processing methods are automatic byproducts of the demand for ever more-powerful ICs. As a result, there are some anticipated yield management challenges. Coinciding with the most recent IC design rule reduction is the long awaited transition to 300 mm processing which presents several unique yield manage ment problems not emphasized before. This paper presents some of the defect sample planning challenges associated with the 300 mm transition and discusses the fundamentals in surmounting them. A key conclusion is the importance of including yield management in the fab planning process from the beginning.

Introduction

The move to 0.13 µm, and the introduction of new materials and processing methods such as copper, low- κ materials, and phase shift reticles, are byproducts of the demand for more powerful ICs. As a result, the yield management challenges are difficult, but somewhat anticipated for a move to a smaller design rule. Some of the associated defect sample planning aspects, such as employing e-beam inspection in addition to optical techniques, have been explored3.

For the first time in recent memory, the semiconductor industry is witnessing the convergence of shrinking design rules, transition to 300 mm, and implementation of new materials in the interconnect scheme such as copper and low-κ dielectric.

Although the transition to new materials and smaller design rules are definitely technology enabling endeavors, such efforts are not without their characteristic yield management challenges. However, many of these challenges would have been encountered without the 300 mm transition taking place simultaneously. Supposing no 300 mm transition were taking place, previously established sample planning exercises could be performed effectively, with little or no change in focus, to establish effective yield management strategies. The fact that the

300 mm transition is taking place, along with other transitions, creates unique challenges and opportunities in yield management that warrant a new focus in defect sample planning. This paper has been organized to reflect those challenges and provide some insight and initiatives to surmounting them. At the outset, a brief overview of the 300 mm technological and process induced challenges are presented followed by a discussion on the classical yield management problem (more specifically defect inspection sampling). A recurring theme is that the layout and automation of the 300 mm facility or fab is vastly different from the conventional 200 mm fabs. Therefore, a significant portion of the paper focuses on the description of the issues relating defect sample planning to fab layout and material movement.

Defect detection challenges in 300 mm

A variety of new challenges to defect detection are introduced during the move from 200 mm to 300 mm.

First, there is the need for detection over a larger surface area. This requires modification of existing hardware.

Second, and more importantly, is the use of new materials. This will change both the composition and type of defects encountered, requiring new techniques for their capture and automatic classification. Third, the size of “killer” defects decreases with the move to a smaller design rule, requiring an increase in tool sensitivity. Fourth, new inspection requirements, such as wafer backside inspection, become important, prompting the redesign of inspection tools. Finally, from a broader perspective, there are issues such as the need for seamless Spring 2001

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information exchange between defect detection and review tools, processing of greater amounts of data, and the need for automation of the defect sampling process, in keeping with the overall fab-automation initiative.

In addition to the above, it is expected, and initial pilot line/ramp experiences confirm, that excursion rates can be higher. To some degree this is merely the fact that excursion frequency is measured in wafers. If one were to measure the frequency in number of dies between excursions, then the rates may be somewhat similar. However, we are not producing one die at a time and, therefore, excursions will occur at shorter intervals then in 200 mm processing. This may require every lot to be sampled at layers where that was not justified in 200 mm processing.

The metrology industry is well on its way in providing the tools and techniques necessary to deal with the above mentioned challenges, but this capability needs to be deployed correctly. With major 300 mm fabs in the planning stages, a unique challenge and opportunity in yield management arises. By including yield management in the planning stage, a fab can be predisposed to deliver superior yields. Further emphasizing the need to include yield management in the planning process is the fact that 300 mm fabs will have processing and inspection tools bound together with various automated material handling systems. This will inherently make fab layouts and material flow less flexible, and emphasizes the need for setting the fabs up correctly the first time. Towards that goal, the following sections address the concepts and methods that should be employed to effectively include defect sample planning in the fab planning stage.

Economies of scale and yield management

The fundamental premise of the 300 mm initiative is economy of scale, i.e. to decrease the manufacturing cost per square centimeter of silicon. It is estimated that the manufacturing cost per square centimeter of silicon will be about 30 percent lower. As one would expect, the pressure on improving yield management to produce more good dies at a lower cost is increased.

It is, however, simplistic to enforce the same cost performance on yield management needs without considering the whole picture. Using the guiding principle of reducing inspection cost per square centimeter of silicon by 30 percent is not the correct metric in which to base the amount of inspection capacity needed. Instead one should seek to maximize the profitability of the fab 42

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and employ the inspection capacity needed to reach that goal. When calculating that capacity, fabs need to pay attention to several factors that collectively are embodied in a yield management strategy. A fundamental analysis of process tool, material handling, and inspection/metrology capacity planning is required.

Furthermore, the impact of inspection on yield and cycle-time needs to be understood to provide a return on investment ( ROI) that is optimal. Strategies will then vary depending on the fab (development or production), device (memory, logic or mixed), and segment (captive or foundry).

The transition to 300 mm has a larger impact on the economic aspect of wafer manufacture. Transferring processes with low baseline yield into ramp phase lack economic viability or, better yet, will be fiscal disasters.

This further reinforces the value of a high yield learning rate being present early. Preliminary analysis shows orders of magnitude difference in the value of yield learning for 300 mm processing. Table 1 contains some of the parameters used and Figure 1 shows the results. It can be observed that there is a much greater return per yield learning percent increase in 300 mm processing than in 200 mm processing. Although a high yield learning rate is not only dependent on the available inspection capacity, a lack of inspection capacity can certainly be the limiting factor in the yield learning process and would most definitely be the differentiator in the long run between leading edge companies and the rest.

ASP/cm2 of Silicon

$40

Wafer starts per week

1000

Die Size

1.5 cm2

Starting D0

0.65/cm2

Fault learning rate per month

4%

Tabl e 1. Parameters in yi eld learn ing rate anal ysi s 1 .

After the ramp-up phase is finished, the excursion control mode of yield management takes over for the full production phase. Again the 300 mm fab is faced with the dilemma that while the initiative provides considerable economies of scale in chip production, each wafer is much more valuable and that greater amounts of material are at risk to excursions than in 200 mm production. Calculating the relative value of 300 mm yield losses relative to 200 mm yield losses in the full production phase is much simpler than for the ramp up

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to locate them in the process, and how frequently to perform the inspections. To answer that, an effective method involves the trade-off between the cost of inspection operations, both fixed and variable, and the cost and/or risk of yield loss due to undetected yieldlimiting defects and process excursions.

The main decision parameters are: • Placement of the inspections (which process steps/process tools) • Type of inspections (test wafer, product, or in-situ inspections) • Inspection frequency (percent lots to sample, number of wafers per lot, area per wafer) • Inspection sensitivity to use F i g u re 1. Comparison of opport unity gai ned in 200 mm and 300 mm p rocessing f or a r ange of increased yield learn ing rat es. The inset explains th e definition of an increased yiel d learning rate, a traditional yield lea rning r ate i s in green an d an incr eased yield lear nin g ra te in orange.

phase. Utilizing the applicable inputs from Table 1, and assuming that the wafers starts per week are 4000 in this phase, we can calculate the value of lost materials each month relative to the same in 200 mm processing (see Figure 2). Numerous results in sample planning analysis2, 3, 7 have shown that the amount of inspection capacity to be used should be based on the value of the materials that can be saved. Given the vast value difference shown in Table 2 it is expected that greater inspection capacity will be needed for the full production phase in 300 mm processing. KLA-Tencor has a well established methodology to do sample planning for both the full production and ramp up phase of the fab. This methodology has the capability to address the 300 mm defect sample planning challenges. The following paragraphs address this methodology and its application to fab planning.

• Which parameters to track and respond to (Statistical Process Control scheme) • The fraction of defects to review • Inspection tool capacity All these parameters are inter-related and each one gives rise to a set of variables that need to be understood. KLA-Tencor’s Sample Planner 3 (SP3) cost model provides the framework and tools to analyze critical fab parameters to develop an optimal inspection strategy with reasonable effort. By joining it with analysis performed during fab planning, the fab plan can be deviced to have inherent advantages in yield management.

In its simplest form, the cost model methodology is based around a recurring in-and-out of control cycle occurring at each step in the process. A cycle starts where each step in the process is assumed to have an in-control mode of operation, which delivers a high

300 mm defect sample planning

It has become well accepted that defect inspection tools play an important role in a fab’s yield management strategy. While few manufacturers currently operate without some type of defect inspection, many IC manufacturers tend to view inspection as non-value added and are overly conservative when planning inspection capacity. It is here that the sample planning problem arises, i.e. what types of inspections to perform, where

F i g u re 2. Examp les of the value of yiel d l osses to ex cursions for 200 mm and 300 mm proces ses duri ng the full production phase.

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F i g u re 3. Typica l questions pos ed i n a sa mple planning analys is.

yield. After a random length of time an excursion takes place, causing lower yields. At this point the inspection sampling strategy determines how quickly the excursion is caught and fixed, restarting the in-and-out of control cycle. It is sought to minimize financial loss by catching the excursions quickly, i.e. minimizing the time between excursion start and detection. It is here that accounting for yield management during fab planning is relevant. A significant portion of the delay to excursion detection is simply the time to get lots to the inspection tools. If a fab has badly placed tools and/or automated material handling systems that cannot accommodate the extra handling loads due to yield management, detection delays can be unnecessarily long and costly. Planning to prevent this type of problems is simply a classic sample planning problem with a greater focus on material handling and cycle-time modeling to provide the data needed that characterize a fab layout. Therefore, outputs of material handling and cycle-time modeling performed during fab planning need to be made available to sample planning analysts, who in turn can give feedback on the current fab plan strengths and weaknesses in excursion detection. Note also the importance of having short detection delays to achieve the accelerated, and very valuable, yield-learning rates.

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Fab planning with sample planner 3

Involving SP3 in fab planning requires the fab to provide good models for material-handling and cycle-time estimation. Then, by combining the outputs of these models with pilot line or applicable 200 mm data to characterize process variance and defect/excursion behavior, SP3 can quantify the yield losses to excursions.

Typical analysis may involve the comparison of farm and hybrid layouts, see Figure 4. A farm layout is where all the metrology tools are kept in a separate bay while a hybrid layout has the metrology tools in the same bay as the process tools they are monitoring. A good materials handling model will be able to provide the travel times as a function of the track layouts, number of stockers, number of automated vehicles, the load on the system, etc. Joining that with a cycle-time model that accounts for processing and queueing times, a comprehensive estimation of how long it will take lots to reach their inspections is realized for both the farm and hybrid layout. SP3 can than use these results to quantify which layout will cause greater yield loss to excursions. Assuming that the material handling system and the number of inspection tools used is the same for both layouts considered, the differentiation comes down to the losses due to excursions. The analysis

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Conclusion

The economies of scale that are achieved with the 300 mm initiative have a flip side when it comes to yield management. The value of the material on each wafer is greater and more sensitive to excursions than ever before, calling for much more careful planning and deployment of inspection capacity. This is particularly relevant given the level of automation that is planned for 300 mm fabs that make it harder to alter layouts after the fact. Unless a fab correctly accounts for yield management during fab planning, there is risk of giving a fab an inherent handicap in yield management and losing considerable amounts of material to excursions. Those losses can significantly affect the gains foreseen from the economies of scale that drive the 300 mm initiative.

References

F i g u re 4. Typical layout s compared in fab planning . F arm layout (left) has all metrology tools i n one bay, Hybri d l ayout ( righ t) h as metr o l o g y tools distributed to f unct ional ar e a s 4 .

can clearly involve greater complexity where the cost of different material handling options and inspection tool capacity needs to be accounted for as well.

Initial 300 mm work and past experience have highlighted the following as the main drivers for inspection capacity: • Fab output (square centimeters of silicon/week) • ASP/product • Excursion frequency, types, magnitude, and yield impact • Tool capability/sensitivity • Material handling in fab/distance to inspection tools

1. Chatterjee, A. Personal Communication, Nov-Dec 2000, K L A - Te n c o r, San Jose, CA. 2. Elliott, R., et al. Sampling plan optimization for detection of lithography and etch CD process excursions. Pro c .

SPIE 2000, vol. 3998, p 527-536.

3. Nurani, R., Gudmundsson, D., Preil, M., Nasongkhla, R., S h a n t h i k u m a r, G. Critical dimension sample planning for sub-0.25 micron processes. Proceedings of the 10th Annual IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop, September 8 - 10, 1999.

4. Nurani, R.K., Gudmundsson, D., Stoller, M., Shanthikumar, G. Intelligent Sampling Strategies for Combined Optical/E-beam inspection. Yield Management Solutions, Vol 2, Issue 2, Spring 2000, p 28.

5. Wright, R. et al., “300 mm Factory Layout and Automated Material Handling”, Solid State Te c h n o l o g y, D e c e m b e r 1999.

6. Campbell, E. et al., “Simulation Modeling for 300 mm Semiconductor Factories”, Solid State Te c h n o l o g y, O c t ober 2000.

7. Williams, R.R., Gudmundsson, D., Monahan, K., Nurani, R., Stoller, M., Shanthikumar, G. Optimized Sample Planning for Wafer Defect Inspection. IEEE Intern a t i o n a l Symposium on Semiconductor Manufacturing, Santa Clara, C a l i f o rnia, October 11-13, 1999.

• Inspection tool throughput/queueing

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