Abstract: Disclosed is a semiconductor fabrication method comprising forming a under layer and a photoresist layer on a substrate, performing an optical proximity correction (OPC) on a layout, and using a photomask manufactured with the corrected layout to form a photoresist pattern on the under layer. The step of performing the OPC comprises generating a first function that comprises a first correction parameter, producing a resist image from the first function, measuring a target error for the resist image, and correcting the first correction parameter into a second correction parameter. The first correction parameter and the second correction parameter comprise a certain number of hydrogen radicals that migrate from the under layer to the photoresist layer.

Abstract: Provided are an optical proximity correction (OPC) method capable of addressing the limitations of patterning and improving the reliability of patterning, and a mask manufacturing method using the OPC method. In the OPC method, a rectangular mask layout for a target pattern is created, the edge of the rectangular mask layout is dissected into segments, a first shape variable point is created, and a second shape variable point is created by shifting the first shape variable point on a rounded target pattern. Thereafter, a curvilinear mask layout is created based on the second shape variable point, a contour is extracted based on the curvilinear mask layout, an edge placement error (EPE) is determined, and the operations are repeated according to a predetermined criterion, thereby realizing a mask layout with minimal EPEs.

Abstract: A method of manufacturing includes receiving a design layout of a target pattern, generating an optical proximity correction (OPC) model the design layout, obtaining an optical proximity corrected (OPCed) design layout by a simulation based on the OPC model, and forming at least one of a mask and a semiconductor device using the OPCed design layout, wherein the generating of the OPC model includes obtaining a vectorized first kernel by vectorizing a first kernel, preparing a first input image by using the design layout of the target pattern, obtaining a vectorized first input image by vectorizing the first input image, obtaining a first rotation image by rotating the vectorized first input image in a first direction, extracting a second kernel by calculating a dot product of the vectorized first kernel and the first rotation image, obtaining a second rotation image by symmetrizing the first rotation image, and extracting a fourth kernel by calculating a dot product of the vectorized first kernel and the secon

Abstract: Provided is a method of optimizing an optical proximity correction (OPC) model, the method including: updating a kernel parameter of the OPC model including a linear combination of a kernel function; extracting a resist image by performing simulation on the OPC model, based on the updated kernel parameter; extracting first linear coefficients of the OPC model by executing Limited memory Broyden Fletcher Goldfarb Shanno with Bounds (L-BFGS-B) based on the resist image; and after the extracting of the first linear coefficients, extracting second linear coefficients of the OPC model by executing Inter Point Optimizer (IPO).

Abstract: Provided are an optimal near-field generation method and a mask manufacturing method comprising the optimal near-field generation method. The optimal near-field generation method may include obtaining a mutual interference complex diffraction pattern formed by mutual interference between a plurality of spherical waves formed as a certain plane wave incident on each of a plurality of edge segments differentiated from an edge of a design layout is scattered on each of the plurality of edge segments, obtaining a complex near-field by applying a Kirchhoff boundary condition to the mutual interference complex diffraction pattern, and obtaining an optimal near-field by optimizing the complex near-field so as to reduce a difference between the complex near-field and a rigorous near-field of the design layout.

Abstract: A method is presented for correcting a photomask includes receiving a target design layout of a semiconductor device. The method includes inferring, by a processor, a mask bias by inputting into a first machine learning model an optical feature value, a geometrical feature value, and a resist feature value of a mask layout based on the target design layout. The processor generates a predicted pattern by incorporating the mask bias in the mask layout, and by comparing the predicted pattern with the target design layout the processor then corrects the mask layout based on a result of the comparison between the predicted pattern and the target design layout.

Abstract: An optical proximity correction (OPC) method using a deep learning-based OPC model having improved performance and a mask manufacturing method including the OPC method are provided. The OPC method includes receiving a design layout of a target pattern, generating a first OPC model reflecting an optical phenomenon in an exposure process, with respect to the design layout, generating a second OPC model reflecting a physical characteristic of a photoresist in the exposure process, and obtaining an optical proximity corrected (OPCed) design layout by performing a simulation using an OPC model including the first OPC model and the second OPC model. Generating the second OPC model uses a first result value obtained by down-sampling an input value by using a sinc filter and a second result value obtained by down-sampling the input value by using erosion.

Abstract: Disclosed are semiconductor fabrication methods and optical proximity correction (OPC) methods. The semiconductor fabrication method comprises performing OPC on a design pattern of a layout to generate a corrected layout, and forming a photoresist pattern on a substrate by using a photomask manufactured with the corrected layout. Performing the OPC includes generating shape points on a contour of the design pattern, producing a hash value of the shape point, selecting a first unique shape point that represents first shape points, determining a first correction bias of the first unique shape point, and creating a correction pattern by applying the first correction bias in common to the first shape points. Producing the hash value includes generating a query range around a target shape point and, based on geometry analysis in the query range, producing the hash value.

Abstract: Disclosed is an operating method of an electronic device for manufacture of a semiconductor device. The operating method includes receiving a layout image of the semiconductor device, generating an intermediate image by generating assist features based on main features of the layout image, evaluating a process result by performing simulation based on the intermediate image, and correcting the intermediate image by correcting shapes of the main features and/or the assist features of the intermediate image based on the process result.

Abstract: Provided are an optical proximity correction (OPC) method capable of addressing the limitations of patterning and improving the reliability of patterning, and a mask manufacturing method using the OPC method. In the OPC method, a rectangular mask layout for a target pattern is created, the edge of the rectangular mask layout is dissected into segments, a first shape variable point is created, and a second shape variable point is created by shifting the first shape variable point on a rounded target pattern. Thereafter, a curvilinear mask layout is created based on the second shape variable point, a contour is extracted based on the curvilinear mask layout, an edge placement error (EPE) is determined, and the operations are repeated according to a predetermined criterion, thereby realizing a mask layout with minimal EPEs.

Abstract: Provided are a method of forming a mask, the method accurately and quickly restoring an image on the mask to the shape on the mask, and a mask manufacturing method using the method of forming the mask. The method of forming a mask includes obtaining first images by performing rasterization and image correction on shapes on the mask corresponding to first patterns on a wafer, obtaining second images by applying a transformation to the shapes on the mask, performing deep learning based on a transformation relationship between ones of the first images and ones of the second images corresponding to the first images, and forming a target shape on the mask corresponding to a target pattern on the wafer, based on the deep learning. The mask is manufactured based on the target shape on the mask.

Abstract: Disclosed is a method of generating a curvilinear sub-resolution assist feature (SRAF) capable of easily generating a curvilinear SRAF satisfying mask rule check (MRC) conditions, an MRC verification method for easy MRC verification of the curvilinear SRAF, and a mask manufacturing method including the method of generating the same. The method of generating a curvilinear SRAF includes generating a curve axis for generating the curvilinear SRAF corresponding to a main feature, generating curve points on a line of the curve axis, and generating the curvilinear SRAF based on the curve points.

Abstract: Disclosed is an operating method of an electronic device for manufacture of a semiconductor device. The operating method includes receiving a layout image of the semiconductor device, generating an intermediate image by generating assist features based on main features of the layout image, evaluating a process result by performing simulation based on the intermediate image, and correcting the intermediate image by correcting shapes of the main features and/or the assist features of the intermediate image based on the process result.

Abstract: Provided are a method of forming a mask, the method accurately and quickly restoring an image on the mask to the shape on the mask, and a mask manufacturing method using the method of forming the mask. The method of forming a mask includes obtaining first images by performing rasterization and image correction on shapes on the mask corresponding to first patterns on a wafer, obtaining second images by applying a transformation to the shapes on the mask, performing deep learning based on a transformation relationship between ones of the first images and ones of the second images corresponding to the first images, and forming a target shape on the mask corresponding to a target pattern on the wafer, based on the deep learning. The mask is manufactured based on the target shape on the mask.