A Neural Network Based Calibration Technique for TI-ADCs with Derivative Information

This paper demonstrates a new neural-network-based calibration technique for inter-channel mismatches of time-interleaved ADCs. By providing with signal value and derivative value of each channel, the network could calibrate the gain mismatch, offset mismatch, and timing mismatch of TI-ADCs. By utilizing signal feature fitting, the ground truth for network training could be obtained without an accurate reference ADC nor a precise ADC error model. Simulation results show that the proposed calibration technique can increase the SFDR of a 14-bit 4Gsps TI-ADC from 32.77 dB to 91.71 dB for single-tone signals, and suppress the maximum spur from -48.51 dBFS to -101.23 dBFS for multi-tone signals. A hardware implementation resources estimation is also given in this paper.