Generalized Method for Extraction of Offset, Gain, and Timing Skew Errors in Time-Interleaved ADCs

Self Cite

This paper presents a novel procedure for detecting offset, gain, and timing skew errors in foreground calibration of time-interleaved analog-to-digital converters (TI-ADCs). An efficient peak detection scheme has been introduced, which is successfully employed to extract timing skew error. The designed method has also been extended to derive the gain and offset errors separately to form the generalized architecture. The objective is performed with the adjustment of the rational coefficient for each error. Simplicity and low hardware consumption will constitute the notable advantages of the proposed algorithm. Mathematical expressions have been provided to explain the principles.A special case in which all triple errors could exist together has also been analyzed. Then, the proposed architecture was implemented at the system level. Finally, the behavioral simulation results for a 12-bit four-channel TI-ADC have been demonstrated to confirm the accuracy of the proposed algorithm. The simulation environment has consisted of the sampling frequency of 8 GHz for TI-ADC and 1 GHz single-tone input frequency for each channel (associated with the Gaussian noise). Based on the results, the maximum value of 77.21 dB for SFDR has been achieved after the calibration process when the average error of 9% for timing skew and 6% tolerance for gain were considered for the channels of TI-ADC.

Section: Hardware Complexity Of the Calibration Schemessupporting

confidence: 80%

“…Also, A and T s represent the amplitude and sampling period, respectively. For the discrete‐time output of the m th channel, we have 19

y_{m} ([], n) = ((), 1 \pm δ_{m}) italicACos ((), ω_{in} ((), n T_{s} + φ_{m})) + {Of}_{m}

Section: The Generalized Algorithmmentioning

confidence: 99%

“…By applying the mathematical expansion of Cos ( α + β ) to (4), 19 we will obtain

y_{m} ([], n) = italicACos ((), ω_{in} ((), n T_{s})) italicCos φ_{m} - italicASin ((), ω_{in} ((), n T_{s})) italicSin φ_{m}

…”

Section: The Generalized Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effective extraction method for triple errors in foreground calibration of TI‐ADCs

Azizian

Ehsanian

2021

Self Cite

“…Since this method is a background calibration method, it has a slow convergence rate compared to foreground calibration methods. In Yuan et al, 11 Hyun-Wook et al, 12 Xiao et al, 13 and Sarkis et al, 14,15 a reference channel is introduced to detect and calibrate timing skews in the TIADC system. However, this introduced reference channel will add additional power consumption and area, and affect the input impedance of the TIADC system.…”

mentioning

confidence: 99%

An 18‐bit 1‐GS/s time interleaved analog‐to‐digital converter with timing skew calibration based on an adaptive genetic algorithm

Zhao

Zhang

et al. 2023

SummaryA foreground calibration method for timing skews of the time interleaved analog‐to‐digital converter (TIADC) system is proposed in this paper. In this method, the adaptive genetic algorithm (AGA) is used to replace the traditional least mean square (LMS) method to detect the timing skews, and the variable delay lines are used to calibrate the timing skews. This method can accomplish the detection and calibration of positive and negative timing skews, and the reference channels are not required. In addition, compared with other methods, this method has higher detection speed and detection accuracy. In this paper, a four‐channel 18‐bit 1‐GS/s TIADC system is modeled, and the performance of the proposed method is further verified. The results show that the convergence speed and the calibration accuracy of this method are higher, the signal‐to‐noise ratio (SNR) of the TIADC system is improved by 65.9 dB after calibration, and this method is also effective when the frequency of the input signal is close to the Nyquist frequency.

Calibration on timing skew mismatch of time‐interleaved ADC based on optimized adaptive genetic algorithm back‐propagation neural network

Liu,

Zhao,

Zhang

et al. 2024

Aiming to address the timing skew mismatch in the time‐interleaved analog‐to‐digital converter (TIADC) system, this paper presents a timing skew mismatch calibration method based on a back propagation (BP) neural network optimized by an adaptive genetic algorithm (AGA). In this paper, a trained BP neural network is used to detect the timing skew mismatch in the TIADC system, and the variable delay line is used to calibrate it. In this paper, AGA is used to optimize the BP neural network, accelerating its training speed and improving the detection accuracy of timing skew mismatch in the system. The proposed approach boasts superior detection speed and accuracy compared to other methods. In this paper, an 18‐bit 1GS/S 4‐channel TIADC system is simulated and the timing skew mismatch in the system is corrected. Simulation results show that the proposed calibration method has fast detection speed, high detection accuracy, and calibration accuracy. After completing the timing skew mismatch correction, the performance of the TIADC system is dramatically improved. The effective number of bits (ENOB) of the system increases by 9.5 bits, and the spurious‐free dynamic range (SFDR) increases by 59.9 dB.