Effects of noise and nonlinearity on the calibration of a non-binary capacitor array in a successive approximation analog-to-digital converter

A successive approximation analog-to-digital converter using a nonbinary capacitor array is presented. A perceptron learning rule is used as the capacitor calibration algorithm. The nonlinearity is analyzed using the Volterra series. The effects of noise and nonlinearity are modeled to verify the calibration robustness. With the presence of noise and nonlinearity, the capacitor weights are adaptively calibrated to match the physical capacitors with better than 22-bit accuracy. The accuracy is no longer limited by capacitor matching.