Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 621.38 Electronics, communications engineering
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016
• Mô tả vật lý: Size: Article No. 24862 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 255792
• Chủ đề: 1. 30 direct energy conversion; 2. 36 materials science; 3. Computational methods; 4. Information science; 5. Inorganic leds; 6. Material science; 7. Scientific data

Tóm tắt

A fundamental challenge in the design of LEDs is to maximise electro-luminescence efficiency at high current densities. We simulate GaN-based LED structures that delay the onset of efficiency droop by spreading carrier concentrations evenly across the active region. Statistical analysis and machine learning effectively guide the selection of the next LED structure to be examined based upon its expected efficiency as well as model uncertainty. This active learning strategy rapidly constructs a model that predicts Poisson-Schr�dinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.