Pattern-dependent effects are a key concern in chemical-mechanical polishing (CMP) processes. In oxide CMP, variation in the interlevel dielectric (ILD) thickness across each die and across the wafer can impact circuit performance and reduce yield. In this work, we present new test mask designs and associated measurement and analysis methods to efficiently characterize and model polishing behavior as a function of layout pattern factors-specifically area, pattern density, pitch, and perimeter/area effects. An important goal of this approach is rapid learning which requires rapid data collection. While the masks are applicable to a variety of CMP applications including back-end, shallow-trench, or damascene processes, in this study we focus on a typical interconnect oxide planarization process, and compare the pattern-dependent variation models for two different polishing pads. For the process and pads considered, we find that pattern density is a strongly dominant factor, while structure area, pitch, and perimeter/area (aspect ratio) play only a minor role.

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A statistical metrology framework is used to identify systematic and random sources of interlevel dielectric thickness variation. Electrical and physical measurements, technology computer-aided design simulations, design of experiments, signal processing, and statistical analysis are integrated via statistical metrology to deconvolve interlevel dielectric thickness variation into constituent variation sources. In this way, insight into planarization variation is enabled; for a representative chemical/mechanical polishing process, we find that die-level neighborhood interactions are comparable to die level feature dependent effects, and that within each die, die level variation is greater than wafer level variation. The characterization of variation sources via statistical metrology is critical for improved process control, interconnect simulation, and robust circuit design.

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