An Effective Digital Calibration Method Based on Volterra Neural Network for Pipeline ADCs

This paper presents an effective digital calibration method based on Volterra neural network (VNN) for pipeline analog-to-digital converters (ADCs). Typically, the nonideality of pipelined ADCs results in dynamic nonlinear systems that are difficult to comprehensively calibrate. The proposed approach combines the strong nonlinear modeling capabilities of Volterra series with the adaptability of neural networks to accurately compensate for nonlinear distortions in pipeline ADCs. The effectiveness of VNN-based calibration is verified in MATLAB and ZYNQ-7000 FPGA. A 625-MS/s 12-bit pipeline ADC in 28 nm CMOS process is presented to verify the proposed calibration technique. The experimental results show that owing to the proposed powerful calibrator, the SFDR and SNDR achieve 79.8 dB and 60.7 dB at low frequencies, 74.3 dB and 59.1 dB at high frequencies, respectively.