A Calibration Scheme for Nonlinearity of the SAR-Pipelined ADCs Based on a Shared Neural Network

This paper proposes a calibration scheme that compensates the nonlinearity of the SAR-Pipelined analog-to-digital converters(ADCs) based on a shared neural network. Due to the fitting ability of the nonlinear functions, the neural network based ADC calibration scheme requires no prior knowledge. Moreover, the introduction of the sharing mechanism not only guarantees the calibration effect for nonlinearity, but also simplifies the hardware complexity, compared to a calibrator with independent neural networks. We validate the scheme with a 14-bit 60MHz SAR-Pipelined ADC fabricated in 28 nm. The measurement results indicate that the ADCs achieve an SFDR of 93.3 dB and an ENOB of 10.63 b, with the assistance of the proposed calibrator. In the meantime, the memory is reduced by 46.7% due to the decrease of neural network parameters, with a sharing rate (ratio of shared quantity to total) of 93.75%.