Abstract: The analog-to-digital converter (ADC) serves as the core component of electronic measurement systems, and its integral nonlinearity (INL) directly affects the overall measurement accuracy. Due to the coupled effects of design, manufacturing process, and temperature variations on INL, achieving fast and accurate testing has become a key research focus. To address the issues of large sampled data volume and low efficiency in existing testing methods, this paper proposes an ADC testing approach based on parametric spectral estimation. The proposed method employs Chebyshev polynomials to fit the ADC input–output characteristic curve, constructing an approximating model of the nonlinear transfer characteristic. The Akaike Information Criterion (AIC) is introduced to enable adaptive selection of the model order, thereby allowing efficient computation of INL parameters through mathematical modeling. Experimental results demonstrate that the proposed method achieves a significant improvement in testing efficiency while maintaining high accuracy, making it particularly suitable for static error analysis of high-resolution ADCs.