High-k Dielectric for Nanoscale MOS Devices

Qian, L. X.

doi:10.1007/978-3-319-50824-5_1

Cited by 1 publication

(1 citation statement)

References 129 publications

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“…Also, the extracted systematic variation is an interpolated map which cannot be described by an analytic expression nor can it be related to process parameters immediately, making it difficult to apply the extracted model for yield prediction. Ref found that, in CMOS fabrication, systematic variations on intrawafer and intradie levels can both be approximately described by a paraboloid function, i.e., a second-order bivariate polynomial. From our observation of the measured photonics wafer, we find that the IWS variation indeed corresponds roughly to such a paraboloid profile, but that it might be more accurate to apply a higher-order radial polynomial model.…”

Section: Method: Hierarchical Spatial Variability Modelmentioning

confidence: 99%

Capturing the Effects of Spatial Process Variations in Silicon Photonic Circuits

et al. 2022

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Silicon photonic devices are very sensitive to process variation, and it is important for circuit designers that they can predict the effect of this variability during the design phase, and optimize their design for both performance and yield. This requires an accurate predictive model of the spatial variations induced by the fabrication process. We present in this paper a method to extract a granular map of the line width and thickness variation on a silicon photonics wafer. We propose a hierarchical model to separate the layout-dependent and location-dependent systematic process variation from the random process variation on different spatial levels. We identify the relative contributions to width and thickness variations and use this to construct a synthetic model for virtual wafers that can be used to analyze the effect on circuit behavior and eventually predict the yield of photonic circuits after fabrication. We observe that the main contribution to waveguide width and thickness variations are systematic, and that die-scale systematic line width variation is correlated with local pattern density.

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Section: Method: Hierarchical Spatial Variability Modelmentioning

confidence: 99%