Joachim Piprek

Utilizing self-consistent numerical simulation in good agreement with measurements, we analyze internal device physics, performance limitations, and optimization options for a unique laser design with multiple active regions separated by tunnel junctions, featuring surprisingly wide InGaN quantum wells. Contrary to common assumptions, these quantum wells are revealed to allow for perfect screening of the strong built-in polarization field, while optical gain is provided by higher quantum levels. However, internal absorption, low p-cladding conductivity, and self-heating are shown to strongly limit the laser performance.

Numerical simulation and machine learning represent opposite approaches to computational analysis of the real world, deductive vs. inductive. However, both methods suffer from various uncertainties and even their combination often fails to link theory and reality. Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates examples of simulation-based machine learning from a physics point of view. Strategies are suggested for achieving more realistic predictions.

Light-emitting diodes (LEDs) based on Gallium Nitride (GaN) have been revolutionizing various applications in lighting, displays, biotechnology, and other fields. However, their energy efficiency is still below expectations in many cases. An unprecedented diversity of theoretical models has been developed for efficiency analysis and GaN-LED design optimization, including carrier transport models, quantum well recombination models, and light extraction models. This invited review paper provides an overview of the modeling landscape and pays special attention to the influence of III-nitride material properties. It thereby identifies some key challenges and directions for future improvements.

Light emitting diodes (LEDs) based on Gallium Nitride (GaN) have been revolutionizing various applications in lighting, displays, medical equipment, and other fields. However, their energy efficiency is still below expectations in many cases. An unprecedented diversity of theoretical models has been developed for efficiency analysis and GaN-LED design optimization. This review paper provides an overview of the modeling landscape and pays special attention to the influence of III-nitride material properties. It thereby identifies some key challenges and directions for future improvements.

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

GaN-based light sources are in high demand for lighting, displays, medical equipment and other applications. InGaN/GaN blue light-emitting diodes (LEDs) reach an electrical-to-optical power conversion efficiency of more than 80% but less than 10% are reported for blue superluminescent light-emitting diodes (SLEDs) and less than 50% for blue laser diodes (LDs). We here analyze the physical mechanisms behind this surprising discrepancy in peak energy efficiency of GaN-based light emitters. Our study reveals that the Mg-doping of group-III-nitride layers, which was pioneered by Isamu Akasaki and collaborators, plays a key role in understanding this efficiency difference.