Accurate Testing of Analog-to-Digital Converters Using Low Linearity Signals With Stimulus Error Identification and Removal

Abstract

Linearity testing of analog-to-digital converters (ADCs) can be very challenging because it requires a signal generator substantially more linear than the ADC under test. This paper introduces the stimulus error identification and removal (SEIR) method for accurately testing ADC linearity using signal generators that may be significantly less linear than the device under test. In the SEIR approach, two imprecise nonlinear but functionally related excitations are applied to the ADC input to obtain two sets of ADC output data. The SEIR algorithm then uses the redundant information from the two sets of data to accurately identify the nonlinearity errors in the stimuli. The algorithm then removes the stimulus error from the ADC output data, allowing the ADC nonlinearity to be accurately measured. For a high resolution ADC, the total computation time of the SEIR algorithm is significantly less than the data acquisition time and therefore does not contribute to testing time. The new approach was experimentally validated on production test hardware with a commercial 16-bit successive approximation ADC. Integral nonlinearity test results that are well within the device specification of /spl plusmn/2 least significant bits were obtained by using 7-bit linear input signals. This approach provides an enabling technology for cost-effective full-code testing of high precision ADCs in production test and for potential cost-effective chip-level implementation of a built-in self-test capability.