Current-Steering DAC Calibration Using Q-Learning

A Q-learning based current-steering digital to analog converter (DAC) calibration method is proposed in this paper for spurious-free dynamic range (SFDR) improvement. A look-up table (LUT) to control the switching sequence of the DAC elements is achieved by Q-learning for an optimal SFDR. Compared with the fixed element transition strategy proposed by manual derivation, the LUT can be updated by off-line training to deal with diverse and complex non-ideal factors limiting the SFDR of DAC. In this paper, a 2.0-GS/s 12-bit segmented current-steering DAC in 28nm process is simulated and the equivalent model is extracted to verify the effectiveness of the method. Simulation results show that, the SFDR over entire Nyquist bandwidth is larger than 70 dB with about 8 dB improvement using the proposed Q-learning method.