LSTM-based framework for predicting point defect percentage in semiconductor materials using simulated XRD patterns.

ABSTRACT: In this paper, we present a machine learning-based approach that leverages Long Short-Term Memory (LSTM) networks combined with a sliding window technique for feature extraction, aimed at accurately predicting point defect percentages in semiconductor materials based on simulated X-ray Diffraction (XRD) data. The model was initially trained on silicon-simulated XRD data with defect percentages ranging from 1 to 5%, enabling it to predict defect percentages from 0 to 10% in silicon and other semiconductor materials, including AlAs, CdS, GaAs, Ge, and ZnS. Through extensive experimentation, we explored different sequence lengths and LSTM units, identifying the optimal configuration as a sequence length of 3501 and 4500 units, which yielded the best results. The model's mean absolute error at

SUBMITTER: Motamedi M 

PROVIDER: S-EPMC11486978 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature