Indirect performance sensing for on-chip self-healing of analog and RF circuits

Shupeng Sun, Fa Wang, Soner Yaldiz, Xin Li, Lawrence Pileggi, Arun Natarajan, Mark Ferriss, Jean Olivier Plouchart, Bodhisatwa Sadhu, Ben Parker, Alberto Valdes-Garcia, Mihai A.T. Sanduleanu, Jose Tierno, Daniel Friedman

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

The advent of the nanoscale integrated circuit (IC) technology makes high performance analog and RF circuits increasingly susceptible to large-scale process variations. On-chip self-healing has been proposed as a promising remedy to address the variability issue. The key idea of on-chip self-healing is to adaptively adjust a set of on-chip tuning knobs (e.g., bias voltage) in order to satisfy all performance specifications. One major challenge with on-chip self-healing is to efficiently implement on-chip sensors to accurately measure various analog and RF performance metrics. In this paper, we propose a novel indirect performance sensing technique to facilitate inexpensive-yet-accurate on-chip performance measurement. Towards this goal, several advanced statistical algorithms (i.e., sparse regression and Bayesian inference) are adopted from the statistics community. A 25 GHz differential Colpitts voltage-controlled oscillator (VCO) designed in a 32 nm CMOS SOI process is used to validate the proposed indirect performance sensing and self-healing methodology. Our silicon measurement results demonstrate that the parametric yield of the VCO is significantly improved for a wafer after the proposed self-healing is applied.

Original languageBritish English
Article number6857434
Pages (from-to)2243-2252
Number of pages10
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
Volume61
Issue number8
DOIs
StatePublished - Aug 2014

Keywords

  • Indirect performance sensing
  • integrated circuit
  • parametric yield
  • process variation
  • self-healing

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