The reliability of numerical simulations very much depends on the physical mechanisms and the material parameters included in the model. Unrealistic results may arise from ignoring relevant physical processes or from using inaccurate parameters. In fact, key parameters are often not exactly known and employed as fit parameters to find agreement with measurements. Read more of this post
Computer simulations of the real world can provide valuable insight but they may also produce unrealistic and misleading results. We need to recognize possible pitfalls if we want to avoid them. All simulations are based on mathematical models. The form of such models varies, from analytical formulas to complex equation systems. However, models always simplify the real world and create a virtual reality in which quite unreal things can happen. Pitfalls loom at all stages of a simulation project: Read more of this post
The influence of two-photon absorption (2PA) on GaAs-based high-power lasers receives increasing attention in recent years. 2PA generates an electron-hole pair by absorbing two photons at once, mainly inside the waveguide layers. In absence of self-heating and catastrophic optical damage, 2PA is considered a possible reason for the strong pulse power saturation that hampers various applications (picture). But published simulations of such saturation often contradict each other.
Dogan et al. reproduced the measured pulse power saturation by including 2PA as well as free-carrier absorption (FCA) in a traveling-wave optical model. This FCA is only caused by 2PA-generated carriers and was found to dominate at pulsed laser powers above 30W. However, the model ignores carrier transport effects such as carrier leakage which was previously established as main cause for the pulse power saturation of the same laser, while neglecting 2PA. More recently, Zeghuzi et al. identified a significant 2PA influence but included a very high gain compression factor in order to match the measured power saturation. Vertical carrier leakage from the active layers, which may lead to strong free-carrier absorption in the waveguide layers, was still ignored. In other words, none of these modeling approaches includes all possible saturation processes simultaneously. Read more of this post
It’s time again to reflect on my peer review experience over the past year. Supported by the availability of high-end commercial software, the number of journal paper submissions on optoelectronic device simulation keeps rising. However, authors often seem to view such software as magic tool that instantaneously delivers realistic results. Mathematical models always simplify reality. But how simple is too simple? Some papers don’t even discuss the underlying theory. There are different levels of simplification possible, which are all based on specific assumptions. Certain assumptions may be inappropriate in the given situation. That is why high-end software packages offer some alternative modeling approaches and let the user decide. In other words, the user should have a detailed understanding of internal device physics and of the models provided by the software.
However, this is only the first step of a successful simulation strategy. The next step is the evaluation of material parameters used in the software. Initial simulation results are typically far off measured characteristics because key parameters are inaccurate. Literature values are quite scattered in some cases. If crucial parameters cannot be measured directly on the device, they should be varied in the simulation until quantitative agreement with measurements is achieved. The model itself may be inadequate if such effort fails or if the fit value is outside the published range. On the other hand, competing models could deliver nearly identical results (see picture) so that more decisive measurements are needed. Such calibration process is often difficult and time-consuming, but in my view, it is the only way to accomplish realistic simulations. Otherwise, calculated results are unreliable and may lead to wrong conclusions. Read more of this post
This pictures provides an atomistic view of an InGaN/GaN quantum well . Inside this quantum well (QW), 17% of the Gallium atoms are replaced by Indium atoms which are here randomly distributed (red dots). Such QWs are employed in many modern light-emitting devices, from full-color displays to LED lamps. The emission wavelength is controlled by the Indium concentration.
QW models and device simulations typically ignore this atomistic structure and assume a uniform QW alloy layer with uniform material properties. Such continuum models still deliver reasonable results in many cases. But some phenomena are hard to explain this way and require an atomistic approach. One example is the much discussed efficiency droop that seems to be mainly caused by strong Auger recombination. Recent studies link this effect to the non-uniformity of InGaN quantum wells (details). Read more of this post