Automated Test Equipment Drift Characterization Based on Gauge Repeatability and Reproducibility

Management of the measurement drift in Automated Test Equipment (ATE) is essential for maintaining good measurement quality while minimizing test interruptions and keeping test costs in check. For the first time, we propose a method to characterize measurement drift that considers the data from both Device Under Test (DUT) and ATE. Proposed approach for ATE drift analysis leverages Gauge Repeatability and Reproducibility (GRR) as an observable. We present the first experimental results on a CAN Transceiver device that confirm that the relationship between GRR and ATE inaccuracies modelled by a-posteriori modifications of measured data, is linear for a broad range of different types of tests. We also discuss further potentials of applying the experimental findings to fine tune the process of ATE calibration and enhance yield analysis wherein a distinction is made out between process variation and measurement inaccuracy due to wearing calibration.