Abstract: Disclosed is a conformal cooling channel design method using a topology optimization design. The conformal cooling channel design method using a topology optimization design according to an embodiment of the present invention includes the steps of: classifying a molded product as a thin structure or a bulk structure, and determining a cooling target area; decomposing the cooling target area into cooling target surfaces of two-dimensional shape; forming cooling channels independent from each other using a topology optimization design for each of the cooling target surfaces; and forming a conformal cooling channel by combining the cooling channels. According to the present embodiment, there is provided a method of designing a conformal cooling channel inside a mold after determining a cooling target surface by classifying the shape of a molded product as a bulk structure or a thin structure and forming an independent cooling channel for each cooling target surface using a topology optimization design.

Abstract: Disclosed is a conformal cooling channel design method using deep learning and a topology optimization design. The conformal cooling channel design method using deep learning and a topology optimization design according to an embodiment of the present invention includes the steps of: classifying a molded product as a thin structure or a bulk structure, and determining a cooling target area; decomposing the cooling target area into cooling target surfaces of two-dimensional shape; producing a preprocessed image by performing a preprocessing step on an image of the cooling target surface to which a thermal load is reflected; forming cooling channels independent from each other, to which a topology optimization design is applied, for each of the cooling target surfaces by inputting the preprocessed image into a previously trained neural network; and forming a conformal cooling channel by combining the cooling channels.

Abstract: A proximity correction method for a semiconductor manufacturing process includes: generating a plurality of pieces of original image data from a plurality of sample regions, with the sample regions selected from layout data used in the semiconductor manufacturing process; removing some pieces of original image data that overlap with each other from the plurality of pieces of original image data, resulting in a plurality of pieces of input image data; inputting the plurality of pieces of input image data to a machine learning model; obtaining a prediction value of critical dimensions of target patterns included in the plurality of pieces of input image data from the machine learning model; measuring a result value for critical dimensions of actual patterns corresponding to the target patterns on a semiconductor substrate on which the semiconductor manufacturing process is performed; and performing learning of the machine learning model using the prediction value and the result value.

Abstract: A proximity correction method for a semiconductor manufacturing process includes: generating a plurality of pieces of original image data from a plurality of sample regions, with the sample regions selected from layout data used in the semiconductor manufacturing process; removing some pieces of original image data that overlap with each other from the plurality of pieces of original image data, resulting in a plurality of pieces of input image data; inputting the plurality of pieces of input image data to a machine learning model; obtaining a prediction value of critical dimensions of target patterns included in the plurality of pieces of input image data from the machine learning model; measuring a result value for critical dimensions of actual patterns corresponding to the target patterns on a semiconductor substrate on which the semiconductor manufacturing process is performed; and performing learning of the machine learning model using the prediction value and the result value.