Dynamic Calibration of Undersampled Pipelined ADCs by Frequency Domain Filtering

Medawar, Samer; Händel, Peter; Murmann, Boris; Björsell, Niclas; Jansson, Magnus

doi:10.1109/tim.2013.2248289

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…It was found that superior static compensation results can be obtained for one of the investigated ADCs if a more representative static model (i.e., HCF modeling) was developed. This observation was subsequently confirmed in [10], where tangible improvements (over [2]) were obtained with a static correction when the actual HCF data were used instead of the HCF model in [9].…”

Section: A State-of-the-art Inl Modelingmentioning

confidence: 63%

“…These actions are not performed in the straight-forward methods developed here, particularly the minimization process. However, the HCF data of ADC1 is very small in magnitude and it was shown that the ADC1 HCF has no perceivable effect on the (static) calibration in [10]. In general, the Gaussian function-based model yielded better modeling results than the sinc approach because it has …”

Section: Model Efficiencymentioning

confidence: 99%

“…These characteristics of the HCF data led the HCF model in (3) to demonstrate a modest performance, necessitating the need for a new HCF modeling methodology, particularly one more capable of representing the HCF component. A low-order polynomial was reinstated to model the LCF in this work because it was shown to effectively represent the LCF data [9], [10], [14]. Two different models were developed to represent the HCF data h[k].…”

Section: B Inl Modeling Preamblementioning

confidence: 99%

See 2 more Smart Citations

Static Integral Nonlinearity Modeling and Calibration of Measured and Synthetic Pipeline Analog-to-Digital Converters

Medawar

Murmann

Händel

et al. 2014

IEEE Trans. Instrum. Meas.

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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 results show that the developed HCF models can achieve virtually complete calibration of the considered ADC.

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Section: A State-of-the-art Inl Modelingmentioning

confidence: 63%

Section: Model Efficiencymentioning

confidence: 99%

Section: B Inl Modeling Preamblementioning

confidence: 99%

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Static Integral Nonlinearity Modeling and Calibration of Measured and Synthetic Pipeline Analog-to-Digital Converters

Medawar

Murmann

Händel

et al. 2014

IEEE Trans. Instrum. Meas.

Self Cite

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“…In [4], a simplified Volterra model was obtained by forcing

h_{i_{1}, i_{2}, \dots, i_{o}}^{o} = 0

for i 2 , i 3 , …, i o ≠ 0 in (1). This model is also used in [9], though in the frequency domain, as described in Subsection III.A in that paper.…”

Section: Reduced‐complexity Volterra Kernelsmentioning

confidence: 99%

Calibration of pipeline ADC with pruned Volterra kernels

et al. 2016

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A Volterra model is used to calibrate a pipeline ADC simulated in Cadence Virtuoso using the STMicroelectronics CMOS 45 nm process. The ADC was designed to work at 50 MSps, but it is simulated at up to 125 MSps, proving that calibration using a Volterra model can significantly increase sampling frequency. Equivalent number of bits (ENOB) improves by 1-2.5 bits (6-15 dB) with 37-101 model parameters. The complexity of the calibration algorithm is reduced using different lengths for each Volterra kernels and performing iterative pruning. System identification is performed by least squares techniques with a set of sinusoids at different frequencies spanning the whole Nyquist band. A comparison with simplified Volterra models proposed in the literature shows better performance for the pruned Volterra model with comparable complexity, improving linearity by as much as 1.5 bits more than the other techniques.

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Integral Nonlinear Fitting and Calibration of High Precision Analog-Digital Converters

Wang¹,

Xu²,

Li³

et al. 2016

2016 13th International Conference on Embedded Software and Systems (ICESS)

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Dynamic Calibration of Undersampled Pipelined ADCs by Frequency Domain Filtering

Cited by 7 publications

References 20 publications

Static Integral Nonlinearity Modeling and Calibration of Measured and Synthetic Pipeline Analog-to-Digital Converters

Static Integral Nonlinearity Modeling and Calibration of Measured and Synthetic Pipeline Analog-to-Digital Converters

Calibration of pipeline ADC with pruned Volterra kernels

Integral Nonlinear Fitting and Calibration of High Precision Analog-Digital Converters

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