Static Integral Nonlinearity Modeling and Calibration of Measured and Synthetic Pipeline Analog-to-Digital Converters
2014 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, Vol. 63, no 3, 502-511 p.Article in journal (Refereed) Published
The integral nonlinearity (INL) modeling of pipeline analog-digital converters (ADCs) is investigated in this paper. The INL is divided into two distinct entities: a low code frequency component (LCF) and a high code frequency (HCF) component. Two static models are developed to represent the INL data. In both models, the LCF component is represented by a low-order polynomial. The HCF modeling is performed using two different basis functions: sinc and Gaussian. The structure of both HCF models is motivated by the pipeline architecture of the ADC under investigation. The model coefficients are estimated by applying the least-squares method to the measured INL data from two samples of a commercial pipeline ADC. The estimated HCF models are compared to each other and to previous models presented in the existing literature. In addition, the modeling methods are applied to synthetic HCF data generated by a pipeline ADC simulation model constructed in Matlab. The INL models are then used to calibrate the synthetic ADCs, and the improvements in spurious free dynamic range (SFDR) are compared to those obtained when the ADCs are compensated by the INL data. Furthermore, the capability of the HCF modeling to calibrate a given ADC is tested by using the HCF model to compensate a synthetically generated ADC output in which only the measured HCF sequence and noise are added to the quantization process. The resultsshow that the developed HCF models can achieve virtually complete calibration of the considered ADC.
Place, publisher, year, edition, pages
2014. Vol. 63, no 3, 502-511 p.
Pipeline ADC, integral nonlinearity, modeling, static calibration, synthetic data, Monte-Carlo simulations.
IdentifiersURN: urn:nbn:se:hig:diva-14921DOI: 10.1109/TIM.2013.2282002ISI: 000331445400001ScopusID: 2-s2.0-84894532758OAI: oai:DiVA.org:hig-14921DiVA: diva2:637556