On-chip variability sensor using phase-locked loop for detecting and correcting parametric timing failures

  • Authors:
  • Kunhyuk Kang;Sang Phill Park;Keejong Kim;Kaushik Roy

  • Affiliations:
  • Intel's Design Technology and Solutions, Hillsboro, OR;Purdue University, West Lafayette, IN;Broadcom Corporation, Tempe, AZ;Purdue University, West Lafayette, IN

  • Venue:
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Year:
  • 2010

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Abstract

Performance variability in digital integrated circuits can largely affect parametric yield and product reliability in ultra deep submicrometer technologies. As a result, variation resilience is becoming an essential design requirement for future technology nodes, especially for timing critical applications. This paper proposes an on-chip variability sensor using phase-locked loop (PLL) to detect process, supply voltage (VDD), and temperature variations (process, voltage, and temperature variation) or even temporal reliability degradation stemming from negative bias temperature instability. Our analysis shows that control voltage (Vcnt) of voltage-controlled oscillator in PLL can be used as a dynamic performance signature of an operating IC. Along with the proposed PLL-based sensor circuit, we also propose a variation-resilient system technique using adaptive body biasing (ABB). The PLL Vcnt signal is efficiently transformed to an optimal body bias signal for various circuit blocks to avoid possible timing failures. Correspondingly, circuits can be designed with significantly relaxed timing constraint compared to conventional approaches, where a large amount of design resources can be wasted to take care of the worst-case situations. We demonstrated our approach on a test chip fabricated in IBM 130-nm CMOS technology. Measurement results show that the PLL-based sensor is cable of tracking various sources of circuit variations. Optimization analysis shows that 42% and 43% reduction in area and power can be obtained using our approach compared to the worst-case sizing. The proposed study refers to our previous study introduced in [1] with major improvements in measurement results and analysis.