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\begin{document}

\title{A Neural Network Based Background Calibration for Pipelined-SAR ADCs at
Low Hardware Cost}



\author[1]{Yuguo Xiang}%
\author[1]{Min Chen}%
\author[1]{Danfeng Zhai}%
\author[1]{Yutong Zhao}%
\author[1]{Junyan Ren}%
\author[1]{Fan Ye}%
\affil[1]{Fudan University}%


\vspace{-1em}



  
  \date{June 17, 2024}


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\begin{abstract}
This paper proposes a background calibration scheme for the
pipelined-SAR ADC based on the neural network. Due to the nonlinear
function fitting capability of the neural network, the linearity of the
ADC is improved effectively. However, the hardware complexity of the
neural network limits its application and promotion in ADC calibration.
Hence, this paper also presents the optimization schemes, including the
neuron-based sharing neural network and the partially binarized with
fixed neural network, in terms of calibration architecture and
algorithm. A 60 MS/s 14-bit pipelined-SAR ADC prototyped in 28-nm
technology is utilized to verify the feasibility of the proposed
calibration method. The measurement results show that the proposed
calibration enhances the SFDR and SNDR from 68.3 dB and 44.6 dB to 95.4
dB and 65.4 dB at low frequency, and from 56.8 dB and 35.6 dB to 90.6 dB
and 63.6 dB at Nyquist frequency. Meanwhile, the original calibrator and
improved calibrator are synthesized in Synopsys Design Compiler to
compare their hardware complexity. Compared with the unoptimized
version, the optimized schemes can decrease the logic area and the
network weights up to 76\% and 52\%, with negligible loss in calibration
performance.%
\end{abstract}%



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