Abstract: A method and apparatus for intra prediction include a prediction unit that is divided into sub-units and predicted in the sub-units. A video decoding method includes: determining whether to split a current block into multiple subblocks; when the current block is split into the multiple subblocks, determining a split direction for the current block between a horizontal split direction and a vertical split direction and the number of the subblocks, based on split information decoded from a bitstream and a width and a height of the current block; reconstructing the current block by sequentially reconstructing the subblocks, that are specified according to the split direction and the number of the subblocks, using intra prediction; and setting a grid of N samples at regular intervals in horizontal and vertical directions and performing deblock-filtering on, among boundaries between the subblocks in the current block, boundaries that coincide with a boundary of the grid.

Abstract: A method of detecting defects of a photoresist pattern includes generating a scanning electron microscope (SEM) image of a surface of a photoresist pattern and signal intensity data relative to pixel position of the surface of the photoresist pattern. The method also includes setting a lower reference intensity threshold value and an upper reference intensity threshold value used as reference values for detecting defects. The method further includes classifying a pixel position of the signal intensity data having a signal intensity value which is less than the lower reference intensity threshold value or greater than the upper reference intensity threshold value as a defect position.

Abstract: There is provided an image decoding method comprising: deriving a first prediction value of a current block by using at least one sample included in a reference block, obtaining an illumination compensation parameter on the basis of a predetermined reference region, deriving a second prediction value of the current block by applying the illumination compensation parameter to the first prediction value and reconstructing the current block on the basis of the second prediction value.

Abstract: A method of detecting defects of a photoresist pattern includes generating a scanning electron microscope (SEM) image of a surface of a photoresist pattern and signal intensity data relative to pixel position of the surface of the photoresist pattern. The method also includes setting a lower reference intensity threshold value and an upper reference intensity threshold value used as reference values for detecting defects. The method further includes classifying a pixel position of the signal intensity data having a signal intensity value which is less than the lower reference intensity threshold value or greater than the upper reference intensity threshold value as a defect position.

Abstract: A method of detecting defects of a photoresist pattern includes generating a scanning electron microscope (SEM) image of a surface of a photoresist pattern and signal intensity data relative to pixel position of the surface of the photoresist pattern. The method also includes setting a lower reference intensity threshold value and an upper reference intensity threshold value used as reference values for detecting defects. The method further includes classifying a pixel position of the signal intensity data having a signal intensity value which is less than the lower reference intensity threshold value or greater than the upper reference intensity threshold value as a defect position.

Abstract: A method of detecting defects of a photoresist pattern includes generating a scanning electron microscope (SEM) image of a surface of a photoresist pattern and signal intensity data relative to pixel position of the surface of the photoresist pattern. The method also includes setting a lower reference intensity threshold value and an upper reference intensity threshold value used as reference values for detecting defects. The method further includes classifying a pixel position of the signal intensity data having a signal intensity value which is less than the lower reference intensity threshold value or greater than the upper reference intensity threshold value as a defect position.

Abstract: Provided is a machine learning-based semiconductor manufacturing yield prediction system and method. A result prediction method according to an embodiment of the present invention comprises: learning different neural network models by classifying different types of data according to their types and respectively inputting the classified different types of data to the different neural network models; and predicting result values by classifying input data according to their types and respectively inputting the classified input data to different neural network models. Therefore, it is possible to apply different neural network models to respective data according to their types, thereby ensuring a neural network model having a structure appropriate for the characteristics of each type of data and thus accurately predicting a result value.

Abstract: A semiconductor device includes a fin-shaped active region protruding from a surface of a base substrate. The fin-shaped active region includes a first impurity region and a second impurity region spaced apart from each other along a first direction and a channel region disposed between the first and second impurity regions. A trench is provided in the base substrate under the channel region. The trench extends in a second direction to intersect the fin-shaped active region in a plan view. A blocking layer fills the trench to overlap with the channel region of the fin-shaped active region. A gate is disposed to overlap with blocking layer and the channel region.

Abstract: A semiconductor device includes a cell mask pattern disposed in a cell region of a mask substrate and a vernier mask pattern disposed in a vernier region of the mask substrate. The vernier mask pattern includes a variable mask pattern portion to transfer a different shape of pattern depending on the magnitude of exposure energy.

Abstract: Lithography processes are provided. The lithography process includes installing a reticle masking (REMA) part having a REMA open region in a lithography apparatus, loading a reticle including at least one reticle chip region in which circuit patterns are disposed into the lithography apparatus, and sequentially exposing a first wafer field, which includes a first chip region corresponding to the reticle chip region, and a second wafer field, which includes a second chip region corresponding to the reticle chip region, of a wafer to rays using the reticle and the REMA part to transfer images of the circuit patterns onto the wafer. An edge boundary of the REMA open region transferred on the first wafer field is located on a scribe lane region between the first and second chip regions while the first wafer field is exposed. Methods of manufacturing a semiconductor device using the lithography process are also provided.

Abstract: A semiconductor device includes a cell mask pattern disposed in a cell region of a mask substrate and a vernier mask pattern disposed in a vernier region of the mask substrate. The vernier mask pattern includes a variable mask pattern portion to transfer a different shape of pattern depending on the magnitude of exposure energy.

Abstract: Lithography processes are provided. The lithography process includes installing a reticle masking (REMA) part having a REMA open region in a lithography apparatus, loading a reticle including at least one reticle chip region in which circuit patterns are disposed into the lithography apparatus, and sequentially exposing a first wafer field, which includes a first chip region corresponding to the reticle chip region, and a second wafer field, which includes a second chip region corresponding to the reticle chip region, of a wafer to rays using the reticle and the REMA part to transfer images of the circuit patterns onto the wafer. An edge boundary of the REMA open region transferred on the first wafer field is located on a scribe lane region between the first and second chip regions while the first wafer field is exposed. Methods of manufacturing a semiconductor device using the lithography process are also provided.

Abstract: A lithography method has a simulation method for mathematically approximating a photoresist film pattern with a Diffused Aerial Image Model (“DAIM”) for semiconductor device fabrication. The DAIM is applied with at least two acids having heterogeneous diffusion characteristics.