Variation is a key concern in semiconductor manufacturing and is manifest in several forms. Spatial variation across each wafer results from equipment or process limitations, and variation within each die may be exacerbated further by complex pattern dependencies. Spatial variation information is important not only for process optimization and control, but also for design of circuits that are robust to such variation. Systematic and random components of the variation must be identified, and models relating the spatial variation to specific process and pattern causes are needed. In this work, extraction and modeling methods are described for wafer-level, die-level, and wafer-die interaction contributions to spatial variation. Waferlevel estimation methods include filtering, spline, and regression based approaches. Die-level (or intra-die) variation can be extracted using spatial Fourier transform methods; important issues include spectral interpolation and sampling requirements. Finally, the interaction between wafer-and die-level effects is important to fully capture and separate systematic versus random variation; spline-and frequency-based methods are proposed for this modeling. Together, these provide an effective collection of methods to identify and model spatial variation for future use in process control to reduce systematic variation, and in process/device design to produce more robust circuits.

In oxide chemical-mechanical polishing (CMP) processes, layout pattern dependent variation in the interlevel dielectric (ILD) thickness can reduce yield and impact circuit performance. Metal-fill patterning practices have emerged as a technique for substantially reducing layout pattern dependent ILD thickness variation. We present a generalizable methodology for selecting an optimal metal-fill patterning practice with the goal of satisfying a given dielectric thickness variation specification while minimizing the added interconnect capacitance associated with metal-fill patterning. Data from two industrialbased experiments demonstrate the beneficial impact of metalfill on dielectric thickness variation, a 20% improvement in uniformity in one case and a 60% improvement in the other case, and illustrate that pattern density is the key mechanism involved. The pros and cons of two different metal-fill patterning practices-grounded versus floating metal-are explored. Criteria for minimizing the effect of floating or grounded metal-fill patterns on delay or crosstalk parameters are also developed based on canonical metal-fill structures. Finally, this methodology is illustrated using a case study which demonstrates an 82% reduction in ILD thickness variation. Index Terms-Chemical-mechanical polishing (CMP), design for manufacturing, metal-fill, within-die variation. I. INTRODUCTION I N recent years, chemical-mechanical polishing (CMP) has emerged as the primary technique for planarizing interlayer dielectrics [1], [2]. Although CMP is very effective at reducing the as-deposited step height and achieves a measure of global planarization not possible with either spin-on or resist etchback Manuscript

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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In this paper, we present a methodology for simulating the impact of wafer-level (within-wafer) and die-level (within-die) variation on circuit performance. For a sample 0.25pm 64x8 SRAM layout, the impact of both die-level and wafer-level poly-CD variation as measured through signal skew and delay is shown to be significant.