Cost-Efficient and Automatic Large Volume Data Acquisition Method for On-Chip Random Process Variation Measurement

Abstract

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 costefficient automatic large data acquisition method. Index Terms-Random process variation, costefficient measurement, automatic large volume data acquisition, statistical characterization, current mode logic driver, current mirror array

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“…15. A 128 × 4 current mirror array to emulate random coefficient errors due to random variation in measuring eye diagram distribution [16]. components and instruments.…”

“…The eye diagram and the bit-error rate were monitored by a high-speed oscilloscope and the BERT. The external reference currents and the 7 bit selection code were digitally controlled by a commercial DAC chip and an FPGA board [16]. With this setup, we first tested the nominal eye size and then used it as the reference for the eye sensitivity and eye variation measurement.…”

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