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This paper presents a conjugate gradient algorithm of convergence speed and accuracy in pipelined ADCs. The pipeline ADC error acts as the objective function of the method. The objective function gradient gradually produce conjugate direction which acts as the search direction to obtain the minimum point, where, the search direction is just a combination of the negative gradient direction with the last iteration of the search direction. It takes only a first derivative information to overcome the slow convergence shortcoming of the steepest descent method. Simultaneously, it can also avoid the disadvantage of storing data and computing Hesse matrix in the Newton method. Simulation results shows that the error converge to minimum point in a short time. The proposed calibration has a small amount of memory required and high stability. © 2016 IEEE.
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