Signature Testing and Diagnosis of High Precision S? ADC Dynamic Specifications Using Model Parameter Estimation

Dynamic testing of high-resolution Sigma Delta (ΔΣ) Analog-to-Digital converters (ADCs) is extremely challenging and expensive since it requires the use of spectrally pure stimulus with at least lOdB better signal-to-noise ratio (SNR) and total harmonic distortion (THD) than the ADC under test. This paper presents a low cost model parameter estimation based test and diagnosis methodology for the dynamic specifications of high-precision ΔΣ ADCs using a multi-tone test input. In the proposed test methodology, the response of a behavioral model of the ADC incorporating its key module level non idealities is matched with the response of the ADC under test by solving a nonlinear optimization problem that finds the best non-ideality parameters corresponding to the observed DUT test response. The dynamic specifications of the ADC are then calculated from the derived model parameters. The multi-tone test stimulus is designed in such a way as to maximize the accuracy with which the model non-ideality parameters can be calculated from the observed test response using a genetic test stimulus optimization algorithm. For test response analysis, the digital pulse sequence at the output of the sigma-delta modulator is made externally observable and utilized as a test access point. A key contribution is that the dynamic specifications such as ENOB, SNR, and THD of the converter as well as module level non-idealities contributing to those specifications can be simultaneously determined from the derived model parameters. The test method is fast and diagnosis is very accurate. Simulation results indicate the validity of the proposed methodology.