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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 box” that instantaneously delivers realistic results. Some papers don’t even discuss the underlying theoretical models. Such models are based on specific assumptions that may or may not be satisfied in the given case. 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 modeling approaches provided by the software.
But 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 incorrect. Literature values are widely 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 inappropriate if such effort fails or if the fit value is outside the published range. Contradicting 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 a realistic simulation. Otherwise, calculated results are unreliable and may lead to wrong conclusions. Read more of this post
Our NUSOD handbook was finally published this month, comprising 1682 pages with 52 chapters written by more than 100 experts from all over the world. I think the handbook format is ideal for beginners but it is also useful for experienced researchers who would like to update and broaden their knowledge in this field.
Mathematical models and simulation methods for optoelectronic devices have experienced a great diversification, mainly driven by the expanding variety of available and envisioned practical applications. Furthermore, the development of commercial software opens the door for a rising number of scientists and engineers to perform sophisticated simulation tasks. However, it is often difficult to identify the best approach to a given problem, considering the ever growing variety and complexity of devices, materials, physical mechanisms, theoretical models, and numerical techniques. Therefore, this handbook offers an introductory yet detailed review of modern optoelectronic device models and simulation techniques. Read more of this post
A record number of 128 papers was presented last week at the NUSOD 2017 conference, stimulating many valuable discussions. Summaries of all presentations are now available online. Poster prizes were awarded to papers MP01, MP25, and MP28. The rump session focused on improved publication methods as recently announced on this blog.
I would like to thank all participants and organizers for making this conference a great success and I hope to see many again next year at the 18th NUSOD conference in Hong Kong.
Whenever you try to answer a research question by using numerical simulation, you start by developing a (or reusing an already developed) mathematical model. Thereafter, you are developing (or reusing already developed) software to perform your numerical simulation. This produces data that you are now analysing and visualising to interpret the discovered results. Finally, you might want to write it all down in an article and publish it on the arXiv and/or in a scientific journal. In this published form the results consist – apart from a couple of figures or tables – mainly of text. In most cases mathematical models, software, data, visualisations and so on are not or not fully shown. This makes it difficult for editors, reviewers and readers alike to fully grasp the research and its results, see e.g. How to get your simulation paper accepted. Moreover, it makes it difficult to validate and in many cases impossible to reproduce the results.
In a time when scholarly publication was limited to printed journals and books it was simply not feasible to provide long rows of numbers not to mention interactive 3D figures or a moving series of pictures. However, with the advent of the digital age and its easy accessible and easy to use infrastructures and tools there is no excuse for not publishing the full research story – and that does not only consist of plain text.
In semiconductor device simulations, one often uses the so-called Boltzmann approximation (an exponential) to link Fermi energy levels to the densities of charge carriers. This corresponds to the classical Einstein relation between diffusion and mobility. However, in organic semiconductors, highly doped regions or at cryogenic temperatures the electron-hole plasma becomes degenerate and Fermi-Dirac statistics can no longer be neglected. The implications are two-fold: First, the distribution function F(η) may no longer be approximated by an exponential (Boltzmann approximation). Second, a generalized Einstein relation now governs the diffusive currents via a nonlinear density-dependent diffusion factor g (the diffusion enhancement). This factor reduces to one in the nondegenerate Boltzmann case and measures the degree of the degeneracy.
The Boltzmann approximation appears in both figures as a straight blue line. For low carrier densities it approximates the Fermi-Dirac integral as well as the Gauss-Fermi integral for organic semiconductors.
The classical Scharfetter-Gummel scheme  in combination with a Voronoï finite volume method provides a numerical solution to the drift-diffusion equations in nondegenerate semiconductors. However, how to approximate the numerical fluxes for degenerate semiconductors (leading to nonlinear diffusion), is still an active area of research. Recently, in  we compared two state-of-the-art numerical flux discretizations:
But which of two is the “best”? Read more of this post