Volterra-Informed Convolutional Neural Network for Wide-Band Pipelined ADC Calibration

High-speed pipelined ADCs face critical nonlinear distortion challenges under process-voltage-temperature (PVT) variations in 5G/6G communications, optical systems, and radar signal processing. To overcome traditional calibration methods’ inherent assumption dependency and linear modeling limitations along with neural networks’ black-box instability and hardware inefficiency, we propose a Volterra-informed convolutional neural network (VICNN) for pipelined ADC calibration. VICNN rigorously models pipelined ADC nonlinear distortions using Volterra polynomial, decomposes the first-, second-, and third-order kernels via 1D convolutional networks, and constructs a multichannel dynamic weight fusion structure. By preserving Volterra polynomials’ time-frequency coupling properties, the method resolves parameter explosion through low-rank decomposition and adaptively adjusts the contribution weights of differentorder kernels, thereby accurately capturing amplitude-frequency and phase-frequency nonlinear coupling under high-frequency inputs. Simulations and measurements conducted on a 12-bit 750 MS/s pipelined ADC model and a silicon-proven 12 -bit $750 \mathrm{MS} / \mathrm{s}$ pipelined ADC show improvements in both the signal-to-noise and distortion ratio (SNDR) and spurious-free dynamic range (SFDR). Notably, the SNDR and SFDR of the silicon-proven pipelined ADC improve from 55.62 dB and 62.32 dB to 57.04 dB and 73.40 dB respectively, achieved with only 181 parameters and $\mathbf{1. 1 8}$ MFLOPs.