Magazine spring00 intelligent

Defect Reduction F

E

A T

U

R

E

S

Intelligent Sampling Strategies for Combined Optical/E-Beam Inspection

by Raman K. Nurani, Dadi Gudmundsson, Meryl Stoller, KLA-Tencor Corporation J. George Shanthikumar, Ph.D., University of California at Berkeley

Today’s advanced IC performance requirements have driven shrinking design rules, high aspect ratio geometry, and multilayered interconnect structures, which in turn have spawned new process technologies, such as dual-damascene, Chemical Mechanical Polishing (CMP), and low k1 lithography techniques. These trends have resulted in challenging process control requirements and accelerated development of new process monitoring technologies such as electron beam (e-beam) inspection. Combined with the above trends, continually shortening product life cycles and eroding market prices are forcing fab/yield managers to achieve higher yields faster and to maintain them at lower wafer processing cost levels than ever before. To meet this challenge, the fab/yield manager needs to address the question: “what is the cost optimal in-line optical and e-beam inspection and control strategy to achieve faster detection and elimination of yield-limiting process problems?”

This article explains the inspection sampling problem, describes the critical need for a data driven scientific approach, and discusses KLA-Tencor’s Sample Planner™. Although the Sample Planner concept is being applied to both defect and metrology yield issues, this article focuses on defect related problems to illustrate the main components of sample planning.

The sample planning problem

It has become well accepted that defect and metrology inspection tools play an important role in a fab’s yield management strategy. It is here that the Sample Planning problem arises, i.e. what combination of inspection tool sets to use, what types of inspections to perform, where to locate them in the process, and how frequently to perform these inspections. The answers to these questions dictate how much inspection capacity is really needed.

The optimum inspection capacity is reached through the trade-off between the cost of inspection and the risk of yield loss due to undetected yield-limiting process problems that inspections could have detected.

28

Spring 2000

Yield Management Solutions

Factors enlarging the problem space

The sample planning problem involves numerous interrelated variables such as process technology, defect mechanisms, inspection equipment, fab logistics, processing parameters, and financial data. Some of these key issues are discussed below.

Changing Defect Population Mix As new process technologies evolve, the types of yieldlimiting process defects and their mixture continually change. The defect Pareto at different process steps for a backend process module of copper technology is illustrated in Figure 1. It is important to know what defect types are present, and to use the data to estimate the

F i g u re 1. Defect ty pes and the ir rela tive densit ies va r y a mong pro c e s s steps. Thi s r e q u i res dif f e rent insp ection tool capabilities at these step s.

A Venn diagram illust rates how e-beam and optical t ools see the defects p resent at barrier/s eed deposi tion.

F

E

A T

U

R

E

S

yield impact of different types. The problem is that the nature of the process step and the defect types, as well as the cost economics, do not make it feasible to use one inspection technology to detect all these defects.

Therefore, a diverse set of inspection tools is needed to provide optimal coverage of all the critical defect types specific to different process steps. This leads to one of the important questions, “What is the right combination of inspection tools to best detect the critical defects?”

Varying Inspection Requirements Making the problem even more complex, the fab’s inspection requirements are not static, they continuously evolve throughout the fab’s operational phases: development, ramp and full production. During development, process transfer and yield learning phases, the main focus is detection and elimination of baseline problems. Whereas during the mature full production phase, when the yield has reached some stability, the focus shifts to maintaining the yield by detecting and eliminating excursions. It is important to note that the defect types, baseline and excursion statistics and their impact and frequencies change from one phase to the other. Also, the changes in the wafer starts and the device average selling price over time need to be accounted for while evaluating the inspection strategy.

Need for a Diverse Set of Inspection Inspection and review with both bright field and dark field optical tools have been routinely used for more than a decade. These tools have played a significant role in the growth of 0.25 µm technology. The sub 0.25 µm technology, with its innovative manufacturing processes and new defect types, has forced inspection equipment manufacturers to develop new inspection technologies such as line monitoring-capable e-beam defect inspection. This technology complements and extends the optical suite of tools by allowing detection of sub-optical physical defects, defects inside high aspect structures with scaled geometry, and buried electrical interconnect defects through voltage contrast that would otherwise remain undetected until end-ofline probing. Additionally, there has been a tremendous enhancement to the value of inspection data, and ultimately to the time to resolve baseline and excursion issues, by the growth in automated defect classification technology. This includes real-time and in-line high resolution methods on the optical and e-beam tools, and automated defect location and high resolution classification using off-line optical and e-beam review tools. All these options need to be quantified and compared to identify which is most economical in a given process, layer, or step.

F i g u r e 2. Th e inspec tion ef fecti veness for diff e rent defects var y with in spection settings.

Varying Inspection Effectiveness The inspection effectiveness is a normalized metric defining the ability of an inspection technology to detect a certain defect type in a given application per unit time. This is mainly a function of capture rate, defect distributions, and tool throughput. Figure 2 illustrates that inspection effectiveness of different inspection technologies varies for different defect types at any given process step. Thus, modeling of each tool technology can help fabs make informed decisions to maximize inspection effectiveness.

Productivity and Yield Trade-offs Although the sampling approaches can increase the throughput of yielding dies, the cycle time may also increase. This impact must be weighed against the potential yield opportunity while determining the optimal inspection capacity. The sample plan evaluation model should be flexible enough to incorporate dynamic changes to current fab factors, such as yield and business conditions, so that the sampling methods can be adapted periodically.

Increasing Set of Decision Variables In addition to deciding which inspection technologies and configurations need to be used for a given process step, more answers to inspection strategy questions are needed such as how many lots to sample, how many wafers per lot, what areas of the wafer need to be inspected, how many and what defects need to be classified and reviewed, should the classification be done in-line or off-line or using some combination of both, what are the right SPC charts, and what are the appropriate control limits.

The solution methodology

Given the multiple factors involved, there is no single inspection strategy applicable to all fabs. For a given Spring 2000

Yield Management Solutions

29

F

E

A T

U

R

E

S

F i g u re 5. Role of combined line monitoring capable e-beam and optical in spection stra tegy dur ing ex cursi on c ontro l .

Evolution of inspection needs

F i g u re 3. Sampl e pl anner appli cation methodology.

fab, Sample Planner analysis can address the numerous variables involved and can make the necessary cost trade-offs to calculate the optimal suite of inspection technologies and their associated sample plans. The most critical input to this analysis is the fab data, from which Sample Planner’s statistical analysis obtains the baseline and excursion statistics, yield impacts, and inspection effectiveness. Using these results, a stochastic model provides the risk and/or cost associated with different sample plans allowing a customized inspection strategy to be crafted for each fab. A diagram of a typical Sample Planner application can be seen in Figure 3.

The Sample Planner methodology has been used successfully with fabs to optimize inspection operations.

Figure 4 presents current application areas. Refer to [13] for specific application methodology and results.

Figure 5 presents some recent results from a simulated case study using a fab‘s data to determine the ideal combination of e-beam and optical inspection for inline monitoring. The key result is that when used in the right combination, the line monitoring-capable ebeam and optical technologies complement each other resulting in superior process control.

F i g u re 4. Current s ampl e planner applicat ion are a s .

30

Spring 2000

Yield Management Solutions

As integrated circuit designs and the required processing techniques continue to evolve, more advanced inspection technology and implementation is required.

The processing equipment needed to accommodate ever smaller design rules and 300 mm wafers will be costly and will increase the cost of yield problems. This, in turn, emphasizes the need for effective inspection technologies and their optimal use in process control. Data driven method-ologies such as the one described here, firmly based on statistical and probability theory, will be increasingly necessary to determine the optimal inspection strategies for emerging industry needs.

Acknowledgments

The authors would to like thank Arun Chatterjee, Rob Cappel, Jeff Hamilton, Mark Keefer, Gus Pinto, and Jay Rathert of KLA-Tencor, Professor Sridhar Seshadri of New York University, and Ruj Nasongkhla of University of California at Berkeley for their valuable inputs and suggestions.

References 1 . R. Elliott, R. K. Nurani, D. Gudmundsson, M. Preil, R. Nasongkhla and J. G. Shanthikumar, “Critical Dimension Sample Planning for Sub 0.25 Micron Processes,” in the p roceedings of Advanced Semiconductor Manufacturing Conference and Workshop (ASMC 99), pp 139-142, Sep 1999.

2 . W. Tomlinson, V. Samek, B. Shifler, D. Gudmundsson, J. M e rritt, R. K. Nurani and J. G. Shanthikumar, “Cost Effective Reticle Quality Management Strategies in Wafer Fabs,” in the proceedings of Advanced Semiconductor Manufacturing Conference and Workshop (ASMC 99), pp 254-258, Sep 1999.

3 . R. Williams, D. Gudmundsson, R. K. Nurani, M. Stoller, A. Chatterjee, S. Seshadri and J. G. Shanthikumar, “Challenging the Paradigm of Monitor Reduction to Achieve Lower Product Costs,” in the proceedings of Advanced Semiconductor Manufacturing Conference and Workshop (ASMC 99), pp 420-425, Sep 1999.