The invention belongs to the field of ADC digital calibration, particularly relates to a pipeline ADC residual network calibration method based on a
genetic algorithm, and aims to solve the problem that the performance of a pipeline ADC is reduced due to coexistence of various errors such as
capacitor mismatch and
comparator imbalance, and the defects that a traditional calibration method is poor in adaptability and poor in nonlinear error calibration capability. The method comprises the following steps: firstly, constructing a multi-frequency noisy
analog signal and inputting the multi-frequency noisy
analog signal into a to-be-calibrated pipeline ADC to obtain a data sample; taking a spurious-free
dynamic range as an
evaluation function, iteratively solving an optimal error compensation value of each data sample through a
genetic algorithm, and further constructing a training
data set; and training a residual network by using the
data set, finally performing error compensation on a to-be-detected sample through a trained model, and outputting a calibrated
signal. The method does not need to depend on the details of the internal architecture of the ADC, can process various linear and nonlinear errors at the same time, remarkably improves the key indexes such as the spurious-free
dynamic range and the significant digits of the ADC, is high in adaptability and generalization ability, and provides an efficient and flexible technical scheme for the calibration of the high-speed and high-precision pipeline ADC.