In-situ characterization and extraction of SRAM variability

Srivatsan Chellappa, Jia Ni, Xiaoyin Yao, Nathan Hindman, Jyothi Velamala, Min Chen, Yu Cao, Lawrence T. Clark

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Measurement and extraction of as fabricated SRAM cell variability is essential to process improvement and robust design. This is challenging in practice, due to the complexity in the test procedure and requisite numerical analysis. This work proposes a new singleended test procedure for SRAM cell write margin measurement. Moreover, an efficient decomposition method is developed to extract transistor threshold voltage (VTH) variations from the measurements, allowing accurate determination of SRAM cell stability. The entire approach is demonstrated in a 90nm test chip with 32K cells. The advantages of the proposed method include: (1) a single-ended SRAM test structure with no disturbance to SRAM operations; (2) a convenient test procedure that only requires quasistatic control of external voltages; and (3) a non-iterative method that extracts the VTH variation of each transistor from eight measurements. The new procedure enables accurate predictions of SRAM performance variability. As validated with 90nm data of write margin and data retention voltage, the prediction error from extracted VTH variations is < 4% at all corners.

Original languageEnglish (US)
Title of host publicationProceedings of the 47th Design Automation Conference, DAC '10
Number of pages6
StatePublished - 2010
Event47th Design Automation Conference, DAC '10 - Anaheim, CA, United States
Duration: Jun 13 2010Jun 18 2010

Publication series

NameProceedings - Design Automation Conference
ISSN (Print)0738-100X


Other47th Design Automation Conference, DAC '10
Country/TerritoryUnited States
CityAnaheim, CA


  • Data retention voltage
  • Extraction
  • SRAM test
  • Threshold voltage variation
  • Write margin

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Modeling and Simulation


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