Abstract: A method is disclosed for forming a diffusion break structure in a fin field effect transistor, including: performing a lithography process on a substrate to form a lithography pattern of a single diffusion break structure; etching the substrate to form an etched single diffusion break structure; performing an epitaxial growth process, forming an epitaxial-layer-covered single diffusion break structure by depositing an epitaxial layer on the surface of the substrate and the bottom surface and sidewalls of the etched single diffusion break structure; etching the epitaxial layer and the substrate in the single diffusion break structure, to form a plurality of fins arranged at intervals; forming an isolation layer between the two adjacent fins, wherein the top surface of the fins is higher than the top surface of the isolation layer; and forming gates spaced apart from each other respectively at the top of the isolation layer.

Abstract: The present application provides a dynamic illumination method based on a scan exposure machine, providing a mask used for exposure and a GDS file corresponding to the mask; dividing pattern information on the mask into n areas with the same width along the direction of movement of the mask during the exposure; performing SMO computation on the pattern information in the n areas, so as to generate n SMO files corresponding to the n areas respectively; performing combinatorial optimization on the n SMO files to obtain a DSMO file; generating a driver of a light source reflector array according to the DSMO file, the illumination; and controlling a reflector array of an exposure machine by calling the driver of the light source reflector array. The DSMO method is performed in each exposure slit area, so as to improve the illumination optimization for a pattern.

Abstract: The disclosure relates to a method for predicting contents of cerium, praseodymium and neodymium components based on virtual samples and a system thereof. The method comprises: obtaining mixed solution of cerium, praseodymium and neodymium in a rare earth extraction process; extracting an H, an S, and an I color feature of a preprocessed image in an HSI color space to obtain an original data sample; constructing a stochastic configuration network model of the content of neodymium component; performing linear midpoint interpolation on the stochastic configuration network model to obtain virtual data samples; fusing original data samples and virtual data samples; reconstructing stochastic configuration network model by using fused data samples; determining content of neodymium component according to reconstructed stochastic configuration network model; and determining contents of cerium and praseodymium according to the content of neodymium component.