Blind adaptive digital background calibration technology of offset in Time-Interleaved ADC

This paper introduces a blind adaptive digital background calibration method designed to correct errors due to offsets between channels in time-interleaved analog-to-digital converters (TI ADCs). By using the DC voltage of one ADC channel as a reference, the method calibrates the offset voltages of the other channels. This approach is notable for its lack of dependence on input signal information and its ability to adapt to variations in channel offsets. A 1 GHz, 12-bit dual-TI ADC is simulated using Simulink to evaluate the effectiveness of the proposed method. The results demonstrate that, despite a 10% channel offset, significant performance improvements were achieved. The Signal-to-Noise and Distortion Ratio (SNDR) increased from 29.4 dB to 68.1 dB, and the Effective Number of Bits (ENOB) improved from 4.59 bits to 11.01 bits. Furthermore, the calibration results remained stable and consistent across an offset range of 0% to 25%.

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Publication Details

Published
2024-09-20
DOI
https://doi.org/10.1109/eit63098.2024.10762655
Primary Topic
Image Processing Techniques and Applications
Type
article
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article

Blind adaptive digital background calibration technology of offset in Time-Interleaved ADC

Jianyang Zhou, Ziyi Chen, Tao Zhang
Image Processing Techniques and Applications
article

Blind adaptive digital background calibration technology of offset in Time-Interleaved ADC

Jianyang Zhou, Ziyi Chen, Tao Zhang
article en

Abstract

This paper introduces a blind adaptive digital background calibration method designed to correct errors due to offsets between channels in time-interleaved analog-to-digital converters (TI ADCs). By using the DC voltage of one ADC channel as a reference, the method calibrates the offset voltages of the other channels. This approach is notable for its lack of dependence on input signal information and its ability to adapt to variations in channel offsets. A 1 GHz, 12-bit dual-TI ADC is simulated using Simulink to evaluate the effectiveness of the proposed method. The results demonstrate that, despite a 10% channel offset, significant performance improvements were achieved. The Signal-to-Noise and Distortion Ratio (SNDR) increased from 29.4 dB to 68.1 dB, and the Effective Number of Bits (ENOB) improved from 4.59 bits to 11.01 bits. Furthermore, the calibration results remained stable and consistent across an offset range of 0% to 25%.

Xiamen University (CN)
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Image Processing Techniques and Applications
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