Edge detection using deep learning pipelines for TEM and SEM metrology by proposing generic and specific approaches

Edge detection is the core of most metrology tools to identify boundaries between materials. With the shrinking in size of all devices for higher performances, this task becomes more and more challenging. In addition, the use of new composition of materials increases the challenge by reducing the contrast between different materials. The general quality of the images is critical to recover the right edges, in particular for High Resolution Electronic Microscopy, which remains the reference for high quality metrology during R&D phases. In these images, the low contrast of the edges and changes in the texture of the materials creating no sharped borders are impacting the performances of classical edge extraction methods. In order to improve capacities of algorithms, we propose a pipeline to generate edge maps. These edge maps can be further used in more classical algorithms to extract better measurements. Two versions of the pipeline can be used, a first one trained on generic images from the semiconductor industry and a second one that can be tuned on specific use cases. The first one provides a ready-to-use solution and the second one is able to provide more accurate measurements at the cost of annotating images. To illustrate these results, we demonstrate the use of these features with various algorithms on use cases from the semiconductor industry such as gates and wires. We propose comparison between the two pipelines both in term of accuracy and time to recipe.