Linewidth Reconstruction Employing a Radial Basis Function Network in Optical Scatterometry

Abstract

This paper applied a radial basis function network (RBFN) in coherent Fourier scatterometry (CFS) to reconstruct the linewidth of periodic line/space (L/S) patterns. The fast, nondestructive, and repeatable measurement capability of CFS enables its integration with intelligent lithography systems. Two steps to reconstruct the linewidth of the L/S patterns were performed in this paper. The first step was to use the finite difference time domain numerical electromagnetic tool to rigorously establish the library of modeled diffraction signatures by using the L/S patterns. Each modeled signature was converted to an intensity vector as the training data to construct the RBFN. The trained RBFN has a simple architecture consisting of three layers: input, hidden, and output layers. The second step was to collect the experimental signatures and feed them into the trained RBFN model to predict the linewidth of L/S patterns. This paper used the transverse electric polarized incident beam at the wavelength of 632 nm in the experimental setup of the CFS. Five L/S patterns were used to test the constructed RBFN. The experimental results indicated that the maximal difference was 13 nm between the CFS and the atomic force microscopy (AFM) measurements for the sample D with an L/S of 200 nm. The minimum difference was 2 nm for the sample A with an L/S of 140 nm. The correlation coefficient between the CFS and AFM metrology measurement running through five samples was 0.972. The high correlation between the CFS with the proposed RBFN measurements and the AFM revealed the potential to implement the radial basis learning kernel in optical metrology to achieve intelligent lithography.