Abstract: According to an aspect of the present inventive concept, a simulation method for a semiconductor fabrication process includes obtaining, as input data, process parameters for controlling a semiconductor process of manufacturing semiconductor devices, or design parameters representing a structure of the semiconductor devices, or both the process parameters and the design parameters; generating predictive data for electrical characteristics of the semiconductor devices using a machine learning model based on the input data; generating reference data for the electrical characteristics of the semiconductor devices using a simulation tool based on the input data; and training the machine learning model using the predictive data and the reference data.

Abstract: A non-transitory computer-readable medium storing a program code including an image generation model, which when executed, causes a processor to input input data including sampling data of some of a plurality of semiconductor dies of a wafer to a generator network of the image generation model and output a wafer map indicating the plurality of semiconductor dies, and to input the wafer map output from the generator network to a discriminator network of the image generation model and discriminate the wafer map.

Abstract: A three-dimensional (3D) modeling method includes obtaining geometric data representing a 3D structure and input parameters including factors determining an attribute of the 3D structure, generating grid data from the geometric data, sequentially generating at least one piece of down-sampled data from the grid data, pre-processing the input parameters to generate a 3D feature map, and generating attribute profile data, representing a profile of the attribute in the 3D structure, from the at least one piece of down-sampled grid data and the 3D feature map based on at least one machine learning model respectively corresponding to at least one stage.

Abstract: Disclosed are methods of predicting semiconductor material properties and methods of testing semiconductor devices using the same. The prediction method comprises preparing a machine learning model that is trained with a training system and using the machine learning model to predict material properties of a target system. The machine learning model is represented as a function of material properties with respect to a descriptor. The descriptor is calculated from unrelaxed charge density (UCD) that is represented by summation of atomic charge density (ACD) of single atoms.

Abstract: According to an aspect of the present inventive concept, a simulation method for a semiconductor fabrication process includes obtaining, as input data, process parameters for controlling a semiconductor process of manufacturing semiconductor devices, or design parameters representing a structure of the semiconductor devices, or both the process parameters and the design parameters; generating predictive data for electrical characteristics of the semiconductor devices using a machine learning model based on the input data; generating reference data for the electrical characteristics of the semiconductor devices using a simulation tool based on the input data; and training the machine learning model using the predictive data and the reference data.

Abstract: A computing device includes memory storing computer-executable instructions; and processing circuitry configured to execute the computer-executable instructions such that the processing circuitry is configured to operate as a machine learning generator configured to receive semiconductor process parameters, to generate semiconductor process result information from the semiconductor process parameters, and to output the generated semiconductor process result information; and operate as a machine learning discriminator configured to receive the generated semiconductor process result information from the machine learning generator and to discriminate whether the generated semiconductor process result information is true.

Abstract: A non-transitory computer-readable medium storing a program code including an image generation model, which when executed, causes a processor to input input data including sampling data of some of a plurality of semiconductor dies of a wafer to a generator network of the image generation model and output a wafer map indicating the plurality of semiconductor dies, and to input the wafer map output from the generator network to a discriminator network of the image generation model and discriminate the wafer map.