Machine learning-enhanced detection of minor radiation-induced defects in semiconductor materials using Raman spectroscopy

Abstract

Radiation damage in semiconductor materials is a crucial concern for electronic applications, especially in the fields of space, military, nuclear, and medical electronics. With the advancements in semiconductor fabrication techniques and the trend of miniaturization, the quality of semiconductor materials and their susceptibility to radiation-induced defects have become more important than ever. In this context, machine learning (ML) algorithms have emerged as a promising tool to study minor radiation-induced defects in semiconductor materials. In this study, we propose a sensitive non-destructive technique for investigating radiation-induced defects using multivariate statistical analyses combined with Raman spectroscopy. Raman spectroscopy is a contactless and non-destructive method widely used to characterize semiconductor materials and their defects. The multivariate statistical methods applied in analyzing the Raman spectra provide high sensitivity in detecting minor radiation-induced defects. The proposed technique was demonstrated by categorizing 100–500 kGy irradiated GaAs wafers into samples with low and high irradiation levels using linear discrimination analysis ML algorithms. Despite the high similarity in the obtained Raman spectra, the ML algorithms correctly predicted the blind testing samples, highlighting the effectiveness of ML in defect study. This study provides a promising approach for detecting minor radiation-induced defects in semiconductor materials and can be extended to other semiconductor materials and devices.