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Title: Optimisation of GaN LEDs and the reduction of efficiency droop using active machine learning

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.
Authors:
 [1] ; ORCiD logo [2] ;  [2] ;  [3]
  1. Univ. of Cambridge (United Kingdom). Dept. of Materials Science and Metallurgy; Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Division
  3. Univ. of Cambridge (United Kingdom). Dept. of Materials Science and Metallurgy
Publication Date:
Report Number(s):
LA-UR-15-27950
Journal ID: ISSN 2045-2322
Grant/Contract Number:
AC52-06NA25396; 20140013DR
Type:
Accepted Manuscript
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Volume: 6; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Cambridge (United Kingdom)
Sponsoring Org:
USDOE Laboratory Directed Research and Development (LDRD) Program
Country of Publication:
United States
Language:
English
Subject:
30 DIRECT ENERGY CONVERSION; 36 MATERIALS SCIENCE; Information Science; Material Science; computational methods; inorganic LEDs; scientific data
OSTI Identifier:
1335601