Abstract: An apparatus for predicting a structure of a semiconductor device, the apparatus includes: at least one processor; a storage configured to store a learned model configured to predict the structure of the semiconductor device; and a memory configured to store at least one code, and at least one processor operatively connected to the memory and configured to execute the at least one code to: input non-destructive metrology data measured from the semiconductor device into the learned model, and predict the structure of the semiconductor device, based on the learned model, wherein the learned model is trained with training data including first data which is non-destructive metrology data and second data which is structural metrology data as reference data of the first data, and wherein the training data is refined based on a similarity of the training data in a space having a first axis corresponding to the first data and a second axis corresponding to the second data as reference axes.

Abstract: Provided are a method of providing an artificial intelligence (AI) algorithm, an operation method of an AI algorithm, an electronic device, a recording medium, and a computer program. The method of providing the AI algorithm includes loading data sets with respect to a spectrum of a semiconductor and a structure of the semiconductor, calculating an out of distribution (OOD) index with respect to the spectrum of the semiconductor, performing data split by clustering sampling the data sets into at least one learning data set with respect to OOD indexes according to semiconductors, and providing an optimal AI algorithm from among a plurality of AI algorithms that have learned the at least one learning data set.

Abstract: A method for measuring a structure based on a spectrum, includes obtaining a first model that includes a first sub-model and a second sub-model following the first sub-model and is trained based on simulation data, generating a second model including a third sub-model identical to the first sub-model, training the second model based on sample spectrum data generated by measuring spectra of sample structures, and estimating, based on the trained second model, the structure from measured spectrum data generated by measuring a spectrum of the structure.

Abstract: A semiconductor wafer fault analysis system includes: a database to store a first reference map, which is classified as a first fault type, and a second reference map, which is classified as a second fault type; a first auto-encoder/decoder to remove a noise corresponding to the first fault type from the first reference map to generate a first pre-processed reference map; a second auto-encoder/decoder to remove a noise corresponding to the second fault type from the second reference map to generate a second pre-processed reference map; and a fault type analyzer. The database is updated based on the first and second pre-processed reference maps, and the fault type analyzer is to classify a fault type of a target map based on the updated database. The target map is generated by measuring a target wafer.

Abstract: A device structure simulation apparatus includes a memory storing a device structure simulation program and a processor configured to execute the device structure simulation program stored in the memory. By executing the device structure simulation program, the device structure simulation apparatus is further configured to receive spectrum data of a target device, generate an input data set by performing preprocessing on the spectrum data, and train a model based on the input data set such that the model is configured to predict a structure of the target device. The preprocessing including selecting a certain basis function based on the spectrum data and separating the spectrum data into sets of certain basis functions, and the model includes at least one sub model.

Abstract: A semiconductor wafer fault analysis system includes: a database to store a first reference map, which is classified as a first fault type, and a second reference map, which is classified as a second fault type; a first auto-encoder/decoder to remove a noise corresponding to the first fault type from the first reference map to generate a first pre-processed reference map; a second auto-encoder/decoder to remove a noise corresponding to the second fault type from the second reference map to generate a second pre-processed reference map; and a fault type analyzer. The database is updated based on the first and second pre-processed reference maps, and the fault type analyzer is to classify a fault type of a target map based on the updated database. The target map is generated by measuring a target wafer.