A convex optimization-based nonlinear filtering algorithm with applications to real-time sensing for patterned wafers

The paper is concerned with nonlinear filtering under unknown dynamics and high-complexity observations. We propose a convex optimization-based filtering algorithm, and show that the algorithm yields a bounded error if the disturbances are small and bounded, and if the observations are redundant. An experimental result is presented to demonstrate that the algorithm is capable of accurate real-time estimation of patterned wafer parameters in a plasma etching process with optical observation.