Evaluation of test measures for LNA production testing using a multinormal statistical model

Abstract

For design-for-test (DFT) purposes, analogue and mixed-signal testing has to cope with the difficulty of test evaluation before production. This paper aims at evaluating test measures for RF components in order to optimize production test sets and thus reduce test cost. For this, we have first developed a statistical model of the performances and possible test measures of the circuit under test (a low noise amplifier). The statistical multi-normal model is derived from data obtained using Monte-Carlo circuit simulation (five hundred iterations). This statistical model is then used to generate a larger circuit population (one million instances) from which test metrics can be estimated with ppm precision at the design stage, considering just process deviations. With the use of this model, a trade-off between defect level and yield loss resulting from process deviations is used to set test limits. After fixing test limits, we have carried out a fault simulation campaign to verify the suitability of the different test measurements, targeting both catastrophic and single parametric faults. Catastrophic faults are modelled by shorts and opens. A parametric fault is defined as the minimum value of a physical parameter that causes a specification to be violated. Test metrics are then evaluated for the LNA case-study. As a result, test metrics for functional measurements such as S-parameters and noise figure are compared with low cost test measurements such as RMS and peak-to-peak current consumption and output voltage, input/output impedance, and the correlation between current consumption and output voltage