The Use of Regularized Least Squares Minimization for the Deconvolution of SEM Images

Abstract

Major advances are currently taking place in image processing algorithm development. When combined with today’s high speed computers utilizing multicore processing and very large memories the door is opened to the practical application of regularization techniques to the restoration of SEM images with improved spatial resolution. It is well recognized that an observed SEM image can be thought of as the convolution of a point spread function (psf) arising from measurement broadening and the true structure imaged. However, even with the abovementioned advances, a variety of additional limitations arise that are preventing the practical implementation of deconvolution to SEM image restoration. These factors include noise as well as a lack of detailed knowledge of the psf since no experimental technique is currently available to directly measure it with level of spatial resolution required. Furthermore, the problem is complicated by other factors such as specimen drift, contamination, the three dimensionality of specimen, signal excitation volume considerations, non-linearity in the scanning system and also nonlinear behavior in the detection chain [1].