Automated rough line-edge estimation from SEM images using deep convolutional neural networks

We propose a deep convolutional neural network named EDGENet to estimate rough line edge positions in low-dose scanning electron microscope (SEM) images corrupted by Poisson noise, Gaussian blur, edge effects and other instrument errors and apply our approach to the estimation of line edge roughness (LER) and line width roughness (LWR). Our method uses a supervised learning dataset of 100800 input-output pairs of simulated noisy SEM rough line images with true edge positions. The edges were constructed by the Thorsos method and have an underlying Palasantzas spectral model. The simulated SEM images were created using the ARTIMAGEN library developed at the National Institute of Standards and Technology. The convolutional neural network EDGENet consists of 17 convolutional, 16 batch-normalization layers and 16 dropout layers and offers excellent LER and LWR estimation as well as roughness spectrum estimation.