A Novel Calibration Algorithm for ADCs Based On Inverse Mapping By Neural Network

This paper proposes a novel calibration method for analog to digital converters (ADCs) based on neural network (NN). In the proposed algorithm, NN is designed to realize the inverse mapping of the non-ideal transfer function of the ADC. An experiment-based spectrum model (EBSM) is established based on the harmonic distortions and spurs caused by non-ideal factors in ADCs. The NN is trained and evaluated with EBSM, and harmonic distortions and spurs be suppressed. The proposed calibration method demonstrates strong portability and can be applied to the calibration of multiple errors in both single-channel and multi-channel ADCs. The calibrator is implemented in Xilinx Virtex-7 FPGA and applied to the online calibration of a 12-bit 150 MS/s pipelined ADC and a 12-bit 600 MS/s time-interleaved ADC. Measurement results indicate that after calibration, the SFDR has improved from 65.85 dB to 82.29 dB, respectively.