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The sigma-delta (SigmaDelta) analog-digital converter (ADC) has been widely used in data conversion applications due to its good performance. However, oversampling and complex circuit behaviors render the transistor-level analysis of these designs prohibitively time consuming. The inefficiency of the standard simulation approach also rules out the possibility of analyzing the impacts of a multitude of environmental and process variations critical in modern VLSI technologies. We present a look-up table (LUT)-based modeling technique to facilitate much more efficient performance analysis of SigmaDelta ADCs. Various transistor-level circuit nonidealities are systematically characterized at the building block level and the whole system is simulated much more efficiently using these building block models. Our approach can provide up to four orders of magnitude runtime speedup over SPICE-like simulators, hence significantly shortening the CPU time required for evaluating system performances such as signal-to-noise-and-distortion ratio. The proposed modeling technique is further extended to enable scalable performance variation analysis of complex SigmaDelta ADC designs. Such modeling approach allows us to perform trade-off analysis of various topologies considering not only nominal performances but also their variabilities. Equally important, with our efficient parametric modeling technique, we are able to feasibly extract simulation-based statistical performance correlation models allowing low-cost alternate linearity test of ADC designs.