Abstract: A method of modeling damages to a crystal caused by an incident particle includes obtaining particle information and crystal information; estimating energy loss of the incident particle based on the particle information and the crystal information; estimating a volume of a vacancy based on the energy loss; estimating a vacancy reaction based on the crystal information and the volume of the vacancy; and generating output data based on the vacancy reaction, the output data including quantification data of the damages.

Abstract: A semiconductor memory device including a transistor body extending in a first horizontal direction and including a first source/drain region, a single-crystal channel layer, and a second source/drain region sequentially arranged in the first horizontal direction, a gate electrode layer extending in a second horizontal direction orthogonal to the first horizontal direction and covering upper and lower surfaces of the single-crystal channel layer, a bit line connected to the first source/drain region, extending in a vertical direction, and having a first width in the second horizontal direction, a spacer covering upper and lower surfaces of the first source/drain region and having a second width greater than the first width, and a cell capacitor on a side opposite to the bit line with respect to the transistor body in the first horizontal direction and including lower and upper electrode layers and a capacitor dielectric layer therebetween may be provided.

Abstract: A method of modeling damages to a crystal caused by an incident particle includes obtaining particle information and crystal information; estimating energy loss of the incident particle based on the particle information and the crystal information; estimating a volume of a vacancy based on the energy loss; estimating a vacancy reaction based on the crystal information and the volume of the vacancy; and generating output data based on the vacancy reaction, the output data including quantification data of the damages.

Abstract: A method for predicting a defect in a semiconductor device includes: calculating a first probability that particles will be generated in a semiconductor element by radiation; calculating a second probability that damage will occur in the semiconductor element due to the particles; generating a training data set using input data and simulation data, the input data including damage data generated using the first probability and the second probability and including at least one of a position in which the damage will occur and an amount of the damage, impurity concentration of impurities doped in at least a portion of the semiconductor element, and structural data of the semiconductor element, and the simulation data including electrical characteristics of the semiconductor element obtained as a result of a simulation based on the input data; and training a machine learning model based on the training data set to generate a defect prediction model.