نوع مقاله : مقاله پژوهشی
نویسندگان
1 گروه فیزیک، دانشکده علوم پایه، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران.
2 گروه فیزیک،دانشکده ی علوم پایه، دانشگاه علوم و فنون هوایی شهید ستاری، تهران، ایران.
کلیدواژهها
موضوعات
عنوان مقاله English
نویسندگان English
Accurate ultraviolet (UV) photodetection is a critical issue in the development of optoelectronic technologies. Conventional semiconductor materials commonly used in UV photodetectors suffer from limitations in terms of response speed, cost, and fabrication complexity. In contrast, UV photodetectors based on quantum dots (QDs), owing to their unique optical and electronic properties, can potentially overcome these limitations. Nevertheless, the synthesis of these nanoparticles is typically based on trial-and-error approaches. In this study, using machine learning models and information extracted from the scientific literature, we investigate and predict the most influential variables governing the synthesis of gallium nitride (GaN) QDs applicable to UV photodetectors. The results obtained from the AdaBoost algorithm indicate that the Ga/N molar ratio, the aluminum-to-metal flux ratio, time, and growth temperature are the most important variables affecting the bandgap of GaN QDs. Moreover, the AdaBoost algorithm is employed to predict the optimal values of the key reaction variables within desired GaN QD bandgap ranges.
کلیدواژهها English
