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This paper presents a production test strategy for digitally-calibrated analog-to-digital converters (ADCs) that incorporate an equalization-based calibration scheme. By analyzing the data obtained in calibration, devices that fail certain static or dynamic specifications can be identified without any additional testing time beyond calibration. The foundation of this test strategy for the ADCs lies on the strong correlations between calibration and functional testing so that devices which violate specifications can be identified by checking the range of steady-state fluctuation in the calibration data. We further develop calibration stimuli to maximize the failing symptoms for fault detection. Simulation results on a pipelined ADC shows that the proposed strategy can effectively pre-screen a good fraction of defective devices that fail static and dynamic specifications including the gain/offset errors and the effective-number of bits (ENOB).