A Calibration Method for Nonlinear Mismatches in M-Channel Time-Interleaved Analog-to-Digital Converters Based on Hadamard Sequences

Liu, Husheng; Wang, Yinan; Li, Nan; Xu, Hui

doi:10.3390/app6110362

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Most existing literature on TIADC nonlinear behaviors are based on polynomial model [23]- [25], which can only model static nonlinearities in TIADC system. We will compare Volterra-based model with polynomial-based model next.…”

Section: A Simulation Resultsmentioning

confidence: 99%

“…So far, however, there has been relatively few papers analyzing and modeling the effects of nonlinearity mismatches in TIADC. A static nonlinear model of TIADC is proposed in [23] and [25], which is based on polynomial and can model several important nonlinearities, like differential nonlinearities (DNL) and integral nonlinearities (INL) [27]. Vogel and Kubin [28] proposed a nonlinear hybrid filter banks to model nonlinear behaviors of TIADC, and illustrated that nonlinear hybrid filter banks can be used to model offset, gain, time and nonlinear mismatch errors.…”

Section: Introductionmentioning

confidence: 99%

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A Behavioral Dynamic Nonlinear Model for Time-Interleaved ADC Based on Volterra Series

Wei

Song

et al. 2019

IEEE Access

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The non-ideal circuit implementations cause a significant degradation in the performance of the time-interleaved analog-to-digital converter (TIADC) system. In this paper, a behavioral model for the TIADC based on Volterra series is proposed to model the dynamic nonlinearities in TIADC. The time-domain and frequency-domain expressions of the behavioral model based on hybrid Volterra series are derived first. Then, the discrete-time equivalent model is proposed by transforming the hybrid TIADC system to a discrete time one based on discrete-time Volterra series only. The derivations give a theoretical foundation to use discrete-time Volterra series to model the mixed-domain TIADC system, which makes it possible to make full use of the related existing derivations, conclusions, and methodologies on discrete-time Volterra series. We also summarize some common special cases of Volterra series to provide practical guidelines for ADC and TIADC practitioners. We present the main features of these models and their relationship with the Volterra series. The simulation and experimental results show the effectiveness of the proposed model. INDEX TERMSAnalog-to-digital converter, time-interleaved, dynamic nonlinearities, Volterra series, discrete-time equivalent model.

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Section: A Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Behavioral Dynamic Nonlinear Model for Time-Interleaved ADC Based on Volterra Series

Wei

Song

et al. 2019

IEEE Access

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“…In reality, electronic mismatches, which periodically modulate the input signal and degrade the output signal's dynamic performance, are inevitable. In recent years, methods have been put forward to mitigate timing mismatches [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17], bandwidth mismatches [18,19], frequency response mismatches [20][21][22][23][24][25][26][27][28][29][30] (frequency response mismatch contains the gain, timing and bandwidth mismatches altogether), and nonlinearity mismatches [31][32][33][34]. To consider all kinds of mismatches, ref.…”

Section: Introductionmentioning

confidence: 99%

Calibration for Sample-And-Hold Mismatches in M-Channel TIADCs Based on Statistics

Liu

Wang

et al. 2019

Applied Sciences

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Time-interleaved analog-to-digital converter (TIADC) is a good option for high sampling rate applications. However, the inevitable sample-and-hold (S/H) mismatches between channels incur undesirable error and then affect the TIADC’s dynamic performance. Several calibration methods have been proposed for S/H mismatches which either need training signals or have less extensive applicability for different input signals and different numbers of channels. This paper proposes a statistics-based calibration algorithm for S/H mismatches in M-channel TIADCs. Initially, the mismatch coefficients are identified by eliminating the statistical differences between channels. Subsequently, the mismatch-induced error is approximated by employing variable multipliers and differentiators in several Richardson iterations. Finally, the error is subtracted from the original output signal to approximate the expected signal. Simulation results illustrate the effectiveness of the proposed method, the selection of key parameters and the advantage to other methods.

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An efficient blind calibration method for nonlinearity mis-matches in <i>M</i>-channel TIADCs

Liu

et al. 2017

IEICE Electron. Express

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As gain, offset, and timing mismatches, nonlinearity mismatches also contribute to spurious components which deteriorate TIADC's performance. This paper proposes an efficient blind calibration method for nonlinearity mismatches in M-channel TIADCs. A modified model for nonlinearity mismatches is established by exploiting binary Hadamard transform (BHT) and differentiator. The calibration is composed of two stagesmismatches compensation and coefficients identification. The principle of mismatches compensation is to reconstruct estimations of the mismatchesinduced spurious components and subtract them from the original TIADC's output. The coefficients identification is performed based on filtered-x least mean square (FxLMS) algorithm. By using improved model and calibration algorithm, the proposed method consumes less computational resource according to the complexity comparison. To tackle the 4-order nonlinearity mismatches in an 16-channel TIADC, the proposed method consumes 23% fewer multipliers than the previous work. Simulation results reveal that both effective resolution and dynamic range improve a lot after calibration.

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A Calibration Method for Nonlinear Mismatches in M-Channel Time-Interleaved Analog-to-Digital Converters Based on Hadamard Sequences

Cited by 3 publications

References 32 publications

A Behavioral Dynamic Nonlinear Model for Time-Interleaved ADC Based on Volterra Series

A Behavioral Dynamic Nonlinear Model for Time-Interleaved ADC Based on Volterra Series

Calibration for Sample-And-Hold Mismatches in M-Channel TIADCs Based on Statistics

An efficient blind calibration method for nonlinearity mis-matches in <i>M</i>-channel TIADCs

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