Veluswami, Senthilkumar (2005-08). Statistical static timing analysis considering process variations and crosstalk. Master's Thesis. Thesis uri icon

abstract

  • Increasing relative semiconductor process variations are making the prediction of realistic worst-case integrated circuit delay or sign-off yield more difficult. As process geometries shrink, intra-die variations have become dominant and it is imperative to model them to obtain accurate timing analysis results. In addition, intra-die process variations are spatially correlated due to pattern dependencies in the manufacturing process. Any statistical static timing analysis (SSTA) tool is incomplete without a model for signal crosstalk, as critical path delays can increase or decrease depending on the switching of capacitively coupled nets. The coupled signal timing in turn depends on the process variations. This work describes an SSTA tool that models signal crosstalk and spatial correlation in intra-die process variations, along with gradients and inter-die variations.
  • Increasing relative semiconductor process variations are making the prediction of
    realistic worst-case integrated circuit delay or sign-off yield more difficult. As process
    geometries shrink, intra-die variations have become dominant and it is imperative to
    model them to obtain accurate timing analysis results. In addition, intra-die process
    variations are spatially correlated due to pattern dependencies in the manufacturing
    process. Any statistical static timing analysis (SSTA) tool is incomplete without a model
    for signal crosstalk, as critical path delays can increase or decrease depending on the
    switching of capacitively coupled nets. The coupled signal timing in turn depends on the
    process variations. This work describes an SSTA tool that models signal crosstalk and
    spatial correlation in intra-die process variations, along with gradients and inter-die
    variations.

publication date

  • August 2005