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.
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB3 0FS, UK.|Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Bertrand
B
Rouet-Leduc
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Kipton
K
Barros
Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
Turab
T
Lookman
Department of Materials Science and Metallurgy, University of Cambridge, Cambridge CB3 0FS, UK.
Colin J
CJ
Humphreys