JSTS:Journal of Semiconductor Technology and Science

The Institute of Electronics and Information Engineers (대한전자공학회)
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Cost-Efficient and Automatic Large Volume Data Acquisition Method for On-Chip Random Process Variation Measurement

  • Lee, Sooeun (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH)) ;
  • Han, Seungho (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH)) ;
  • Lee, Ikho (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH)) ;
  • Sim, Jae-Yoon (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH)) ;
  • Park, Hong-June (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH)) ;
  • Kim, Byungsub (Dept. of Electronic and Electrical Engineering Pohang University of Science and Technology (POSTECH))
  • Received : 2014.08.20
  • Accepted : 2015.01.01
  • Published : 2015.04.30
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Abstract

This paper proposes a cost-efficient and automatic method for large data acquisition from a test chip without expensive equipment to characterize random process variation in an integrated circuit. Our method requires only a test chip, a personal computer, a cheap digital-to-analog converter, a controller and multimeters, and thus large volume measurement can be performed on an office desk at low cost. To demonstrate the proposed method, we designed a test chip with a current model logic driver and an array of 128 current mirrors that mimic the random process variation of the driver's tail current mirror. Using our method, we characterized the random process variation of the driver's voltage due to the random process variation on the driver's tail current mirror from large volume measurement data. The statistical characteristics of the driver's output voltage calculated from the measured data are compared with Monte Carlo simulation. The difference between the measured and the simulated averages and standard deviations are less than 20% showing that we can easily characterize the random process variation at low cost by using our cost-efficient automatic large data acquisition method.

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References

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