Abstract: Scanning electron microscope (SEM) metrology is critical in semiconductor manufacturing for patterning process quality assessment and monitoring. Besides feature width and feature-feature space dimension measurements from critical dimension SEM (CDSEM) images, visual inspection of SEM image also offers rich information on the quality of patterning. However, visual inspection alone leaves considerable room of ambiguity regarding patterning quality. To narrow the room of ambiguity and to obtain more statistically quantitative information on patterning quality, SEM-image contours are often extracted to serve such purposes. From contours, important information such as critical dimension and resist sidewall angle at any location can be estimated. Those geometrical information can be used for optical proximity correction (OPC) model verification and lithography hotspot detection, etc. Classical contour extraction algorithms based on local information have insufficient capability in dealing with noisy and low contrast images. To achieve reliable contours from noisy and low contrast images, information beyond local should be made use of as much as possible. In this regard, deep convolutional neural network (DCNN) has proven its great capability, as manifested in various computer vision tasks. Taking the full advantages of this maturing technology, we have designed a DCNN network and applied it to the task of extracting contours from noisy and low contrast SEM images. It turns out that the model is capable of separating the resist top and bottom contours reliably. In addition, the model does not generate false contours, it also can suppress the generation of broken contours when ambiguous area for contour extraction is small and non-detrimental. With advanced image alignment algorithm with sub-pixel accuracy, contours from different exposure fields of same process condition can be superposed to estimate process variation band, furthermore, stochastic effect induced edge placement variation statistics can easily be inferred from the extracted contours.
Keywords: SEM images; contour extraction; machine leaning (ML); deep convolution neural network (DCNN); edge placement variation