The determination of measurement uncertainty is of paramount importance in defining the range of values assigned to a given response variable. In electronic circuits, this is a challenging task that still needs further development. Difficulties associated with repeated measurements and the inherent variability of components make it difficult to determine uncertainty unless a large number of measurements are taken, which increases costs. Furthermore, due to the strong nonlinear characteristics of most electronic circuits and the difficulty in determining a differentiable expression, it is not feasible to apply uncertainty propagation. An alternative for modeling uncertainty is the Monte Carlo method, as proposed by the Guide for the Expression of Measurement Uncertainty (GUM and GUM-S1). However, when the probability distribution of the response variables significantly deviates from normal, the GUM-S1 method may lead to inaccurate results. This study proposes a new Monte Carlo-based method to quantify measurement uncertainty in electronic circuits and includes a comparative study of GUM, GUM-S1, and the proposed method. It is shown that uncertainty can be determined more accurately with the proposed method when normality cannot be assumed. The proposed method is applied to two analog circuits: a Sallen-Key filter and a low-signal amplifier.
