Nonlinear error analysis and calibration model for cyclic ADCs in large array CMOS image sensors

Abstract A mathematical model is established to study the impact of nonlinear errors on the performance of cyclic analog-to-digital converters (ADCs) and imaging quality of CMOS image sensors (CIS). And a radix-based foreground digital calibration method for cyclic ADCs in large array CIS is proposed and modeled. The nonlinear errors caused by capacitor mismatch and finite operational amplifier (OPAMP) gain are mainly studied. Simulation results show that the proposed calibration method based on the statistical characteristics of large array CIS can effectively reduce the influence of nonlinear errors, greatly improve ADC linearity under large capacitor mismatch and finite OPAMP gain. In a CIS with 1500 columns of parallel ADCs, when the capacitor mismatch and OPAMP gain are 1% and 40 dB respectively, the effective number of bits (ENOB) of the calibrated ADC can be increased from about 6bit to 13bit. These results provide a new guideline for the calibration of cyclic ADCs in large array CMOS image sensors.