Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning
- Univ. of Cambridge (United Kingdom). Dept. of Materials Science and Metallurgy; Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
- Univ. of Cambridge (United Kingdom). Dept. of Materials Science and Metallurgy
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-Schrödinger simulations of devices, and that simultaneously produces structures with higher simulated efficiencies.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Univ. of Cambridge (United Kingdom)
- Sponsoring Organization:
- USDOE Laboratory Directed Research and Development (LDRD) Program
- Grant/Contract Number:
- AC52-06NA25396; 20140013DR
- OSTI ID:
- 1335601
- Report Number(s):
- LA-UR-15-27950
- Journal Information:
- Scientific Reports, Vol. 6; ISSN 2045-2322
- Publisher:
- Nature Publishing GroupCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 38 works
Citation information provided by
Web of Science
Web of Science
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