Abstract: An auto calibration method according to at least one example embodiment may include receiving at least one internal equation, input data associated with a semiconductor device design, and hardware data associated with the semiconductor device design, generating at least one approximation function based on the input data and the hardware data, determining at least one loss function based on the generated at least one approximation function, determining at least one parameter of the at least one approximation function and at least one parameter of the at least one internal equation such that a value of the loss function is 0, and selectively adjusting the semiconductor device design based on the determined at least one parameter of the at least one approximation function and the at least one parameter of the at least one internal equation.

Abstract: A method of generating a neural network model and performing a circuit simulation by using the neural network model is presented. The method includes generating sample data by performing a process simulation based on a temperature and a process parameter, training the neural network model based on the sample data, performing a lightweight operation on the neural network model to generate a lightweight neural network model, re-training the lightweight neural network model, and performing the circuit simulation with the process parameter as an input by using the re-trained lightweight neural network model.

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: Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

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 method of generating optimal input data for a design simulator providing output data related to output parameters in response to input data related to input parameters. The method includes; generating training data including sample input data and sample output data, selecting at least one essential input parameter affecting a plurality of output parameters from among the input parameters in accordance with an estimation model trained using the training data, and generating the optimal input data in accordance with essential input data corresponding to the at least one essential input parameter and the sample output data.

Abstract: Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

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: Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

Abstract: A method of generating a simulation model based on simulation data and measurement data of a target includes classifying weight parameters, included in a pre-learning model learned based on the simulation data, as a first weight group and a second weight group based on a degree of significance, retraining the first weight group of the pre-learning model based on the simulation data, and training the second weight group of a transfer learning model based on the measurement data, wherein the transfer learning model includes the first weight group of the pre-learning model retrained based on the simulation data.

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: Provided is a simulation method performed by a process simulator, implemented with a recurrent neural network (RNN) including a plurality of process emulation cells, which are arranged in time series and configured to train and predict, based on a final target profile, a profile of each process step included in a semiconductor manufacturing process. The simulation method includes: receiving, at a first process emulation cell, a previous output profile provided at a previous process step, a target profile and process condition information of a current process step; and generating, at the first process emulation cell, a current output profile corresponding to the current process step, based on the target profile, the process condition information, and prior knowledge information, the prior knowledge information defining a time series causal relationship between the previous process step and the current process step.

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 method for a hybrid model that includes a machine learning model and a rule-based model, includes obtaining a first output from the rule-based model by providing a first input to the rule-based model, and obtaining a second output from the machine learning model by providing the first input, a second input, and the obtained first output to the machine learning model. The method further includes training the machine learning model, based on errors of the obtained second output.

Abstract: A fault analysis method of a semiconductor fault analysis device is provided. The fault analysis method includes: receiving measurement data measured corresponding to a semiconductor device; generating double sampling data based on the measurement data and reference data; performing a fault analysis operation with respect to the double sampling data; classifying a fault type of the semiconductor device based on a result of the fault analysis operation; and outputting information about the fault type.

Abstract: A fault analysis method of a semiconductor fault analysis device is provided. The fault analysis method includes: receiving measurement data measured corresponding to a semiconductor device; generating double sampling data based on the measurement data and reference data; performing a fault analysis operation with respect to the double sampling data; classifying a fault type of the semiconductor device based on a result of the fault analysis operation; and outputting information about the fault type.