A Neural Network-based Digital Calibration Improvement Method for ADC

The article proposes a neural network-based digital calibration improvement method for ADC, targeting the problems of large network size and computation complexity in neural network-based ADC digital calibration. The proposed method involves three aspects: changing the neural network output value to be fitted, optimizing gradient descent with a simulated annealing algorithm, and using half-precision parameters. Improvements 1 and 2 slightly increase the algorithm complexity, while improvement 3 reduces the multiplier area and power consumption. Experimental results show that Improvement 1 significantly reduces the required number of training iterations for convergence, Improvement 2 improves the stability of neural network training, and Improvement 3 does not significantly degrade algorithm performance. The proposed method has low implementation complexity and can provide guidance for optimizing neural network digital calibration circuits on chips or FPGAs.