This article presents a method to calibrate a 16-channel 40 GS/s time-interleaved analog-to-digital converter (TI-ADC) based on channel equalization and Monte Carlo method. First, the channel mismatch is estimated by the Monte Carlo method, and equalize each channel to meet the calibration requirement. This method does not require additional hardware circuits, every channel can be compensated. The calibration structure is simple and the convergence speed is fast, besides, the ADC is worked in background mode, which does not affect the conversion. The prototype, implemented in 28 nm CMOS, reaches a 41 dB SFDR with an input signal of 1.2 GHz and 5 dBm after the proposed background offset and gain mismatch calibration. Compared with previous works, the spurious-free dynamic range (SFDR) and the effective number of bits (ENOB) are better, the estimation accuracy is higher, the error is smaller and the faster speed of convergence improves the efficiency of signal processing.
Citation: Yongjie Zhao, Sida Li, Zhiping Huang. TI-ADC multi-channel mismatch estimation and calibration in ultra-high-speed optical signal acquisition system[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 9050-9075. doi: 10.3934/mbe.2021446
This article presents a method to calibrate a 16-channel 40 GS/s time-interleaved analog-to-digital converter (TI-ADC) based on channel equalization and Monte Carlo method. First, the channel mismatch is estimated by the Monte Carlo method, and equalize each channel to meet the calibration requirement. This method does not require additional hardware circuits, every channel can be compensated. The calibration structure is simple and the convergence speed is fast, besides, the ADC is worked in background mode, which does not affect the conversion. The prototype, implemented in 28 nm CMOS, reaches a 41 dB SFDR with an input signal of 1.2 GHz and 5 dBm after the proposed background offset and gain mismatch calibration. Compared with previous works, the spurious-free dynamic range (SFDR) and the effective number of bits (ENOB) are better, the estimation accuracy is higher, the error is smaller and the faster speed of convergence improves the efficiency of signal processing.
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