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Connecting Theory and Practice in Optoelectronics
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
Topological insulators have attracted a huge amount of attention in the field of condensed matter physics. This new state of matter is characterized by a bulk band gap and conducting surface states. The surface states have linear dispersion resembling relativistic Dirac fermions, in analogy with graphene. In contrast to graphene the 2D fermions on the surface are non-degenerate, whereas electrons in graphene are spin and valley degenerate. The linear dispersion and 2D nature of the electrons in graphene leads to the universal optical absorbance απ≈2.3% given by the fine structure constant , independent on the material parameters and the photon energy. Due to this relatively large absorption for a single atomic layer, graphene is a promising material for optoelectronic applications e.g. photodetectors, which has been demonstrated . Similarly a topological insulator has an absorbance of απ/2≈1.1% for photon energies below the bulk band gap, due to the surface states at the top and bottom surface. It has been shown that the signal-to-noise ratio of photodetector based on a thin slab of the topological insulator Bi2Se3 can be significantly larger than for a graphene based device. For a slab thickness below 6 nm the surface states on opposing sides interact leading to a band gap, which can be tuned by varying the thickness. However, this way of manipulating the optical properties is not very flexible since Bi2Se3 has a layered structure with five atomic layers strongly bound in a quintuple layer, limiting the possible thicknesses to only integer numbers of quintuple layers (QL). If instead strain is used to tune the optical properties, this can be done continuously and dynamically.
Looking back at 2016, I just realized that my yearly load of peer reviews has increased to almost 80 journal papers, mainly in the field of optoelectronic device simulation. The rising number of such paper submissions to top journals is certainly good news, but the paper quality is often insufficient. Unfortunately, I have to propose rejection of most papers after a detailed assessment of essential mistakes. A fundamental mistake in my view is the unproven assumption that simulations represent the real world. Authors often don’t seem to understand that computer simulations lead us into a virtual reality in which many unreal effects can happen – depending on their choice of mathematical models and material parameters.
In the last two decades, there has been an increasing interest in multiscale modeling applied to electronic devices. Several factors are driving this trend. On the one hand, device dimensions of “classical” devices like MOSFETs have continuously been scaled down in order to increase device performance. On the other hand, specific properties of quantum structures are systematically utilized in modern devices. The embedding of the active device region in its environment including access regions and contacts, and the mutual interaction between different aspects like optics, thermal heating, strain and carrier transport requires an involved multiscale/multiphysics simulation approach which can handle different physical models and different length or time scales.