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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.