  We present a Newton-like method to solve inverse problems and to quantify
parameter uncertainties. We apply the method to parameter reconstruction in
optical scatterometry, where we take into account a priori information and
measurement uncertainties using a Bayesian approach. Further, we discuss the
influence of numerical accuracy on the reconstruction result.
