Modeling ADC Nonlinearity in Monte Carlo Procedures for Uncertainty Estimation

Monte Carlo procedures can be successfully employed to evaluate the uncertainty of measurements performed by digital processing of sampled data, provided that the uncertainties affecting the input samples are modeled correctly. The static nonlinearity is the most difficult error to be modeled, since the technical specifications provided by the manufacturers of the acquisition systems are not usually sufficient to describe the nonlinearity curve over the entire input range. Thus, suitable assumptions are needed and approximations are unavoidable. This paper focuses on measurement systems based on plug-in data acquisition boards, which are generally based on successive approximation register analog-to-digital conversion (ADC). A behavioral model is presented, according to which the overall nonlinearity is divided into two contributions: a smooth component, responsible for the macroscopic error trend in the output domain, and a component with sudden variations in the scale of values. Theoretical fundamentals of the method are reported, and experimental results highlighting the reliability of the proposed approach are discussed.