Uncertainty and sensitivity analysis and its applications in OCD measurements

This article describes an Uncertainty & Sensitivity Analysis package, a mathematical tool that can be an effective time-shortcut for optimizing OCD models. By including real system noises in the model, an accurate method for predicting measurements uncertainties is shown. The assessment, in an early stage, of the uncertainties, sensitivities and correlations of the parameters to be measured drives the user in the optimization of the OCD measurement strategy. Real examples are discussed revealing common pitfalls like hidden correlations and simulation results are compared with real measurements. Special emphasis is given to 2 different cases: 1) the optimization of the data set of multi-head metrology tools (NI-OCD, SE-OCD), 2) the optimization of the azimuth measurement angle in SE-OCD. With the uncertainty and sensitivity analysis result, the right data set and measurement mode (NI-OCD, SE-OCD or NI+SE OCD) can be easily selected to achieve the best OCD model performance.