Bayesian Fault Diagnosis of RF Circuits Using Nonparametric Density Estimation

This paper discusses a Bayesian fault diagnosis scheme for RF circuits. We use non-idealized spot defect models by taking into account both their resistive and capacitive behavior at the layout level. The likelihoods in the Bayes rule are estimated using nonparametric kernel density estimation. Our case study is an RF low noise amplifier. The diagnosis decisions and the subsequent defect ambiguity analysis are demonstrated using post-layout simulations.