Methodology for efficient substrate noise analysis in large-scale mixed-signal circuits

  • Authors:
  • Emre Salman;Renatas Jakushokas;Eby G. Friedman;Radu M. Secareanu;Olin L. Hartin

  • Affiliations:
  • Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY;Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY;Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY;Freescale Semiconductor, Microwave and Mixed-Signal Technologies, Tempe, AZ;Freescale Semiconductor, Microwave and Mixed-Signal Technologies, Tempe, AZ

  • Venue:
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Year:
  • 2009

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Abstract

A methodology is proposed to efficiently analyze substrate noise coupled to a sensitive block due to an aggressor digital block in large-scale mixed-signal circuits. The methodology is based on identifying voltage domains on the substrate by exploiting the small spatial voltage differences on the ground distribution network of the aggressor circuit. Specifically, similarly biased regions on the substrate short-circuited by the ground network are determined, and each of these regions is represented by a single equivalent input port to the substrate. The remaining ports within that domain are ignored to reduce the computational complexity of the extraction process. An algorithm with linear time complexity is proposed to merge those substrate contacts exhibiting a voltage difference smaller than a specified value, identifying a voltage domain. An equivalent contact is placed at the geometric mean of the merged contacts, ignoring all of the remaining ports such as the source/drain junctions of the devices. The ground network impedance is updated for each merged contact based on the proposed algorithm to maintain sufficient accuracy of the noise voltage. The substrate with reduced input ports is extracted using an existing extraction tool to analyze the noise at the sense node. As compared to the full extraction of an aggressor circuit, the methodology achieves a reduction of more than four orders of magnitude in the number of extracted substrate resistors with a peak-to-peak error of 24%.