Statistical Run-Time Verification of Analog Circuits in Presence of Noise and Process Variation

Noise and process variation present a practical limit on the performance of analog circuits. This paper proposes a methodology for modeling and verification of analog designs in the presence of shot noise, thermal noise, and process variations. The idea is to use stochastic differential equations to model noise in additive and multiplicative form and then combine process variation due to 0.18 μm technology in a statistical run-time verification environment. The efficiency of Monte-Carlo and Bootstrap statistical techniques are compared for a Colpitts oscillator and a phase locked loop-based frequency synthesizer circuit.