Statistically Validating the Impact of Process Variations on Analog and Mixed Signal Designs

Process variation presents a practical challenge on the performance of analog and mixed signal (AMS) circuits. This paper proposes a Monte Carlo-Jackknife (MC-JK) technique, a variant of Monte Carlo method, to verify process variation affecting the performance and functionality of AMS designs. We use a behavioral model to which we encompass device variation due to $65nm$ technology process. Next, we conduct hypothesis testing based on the MC-JK technique combined with Latin hypercube sampling in a statistical run-time verification environment. Experimental results demonstrate the robustness of our approach in verifying AMS circuits.