A neural network based background calibration for pipelined-SAR ADCs at low hardware cost

This paper proposes a background calibration scheme for the pipelined-Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) based on the neural network. Due to the non-linear 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 greatly enhances the Spurious Free Dynamic Range (SFDR) and Signal-to-Noise-and-Distortion Ratio (SNDR) from low frequency to 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.