• Computational Photonics

    Find out how computational photonics models can help to understand the impact factors governing photonic systems and applications and can hence be used to explore new application designs. The necessary computations can range from "back-of the envelope" calculations to comprehensive simulation models. We are using Microsoft Excel to study photonics problems and to design, analyze, and optimize photonics applications.

    Below we show some typical results obtained with Excel based computational models.

  • FDTD Pulse Propagation

    This animation shows the propagation of an electromagnetic pulse across a dielectric boundary. It can be clearly seen that part of the pulse is reflected at the boundary, whereas a pulse with reduced amplitude propagates with lower propagation velocity in the medium with higher dielectric constant. The simulation was performed with a 1D-FDTD algorithm implemented in Excel (click title to view).

  • Schrödinger's Equation

    This animation visualizes the evolution of the wave function of a particle confined in a 2D-potential well. Shown is the square of the wave-function, which - according to the Copenhagen interpretation - reflects the probability of finding the quantum system in a certain position at a certain time. It is demonstrated how the wave-function is reflected at the potential well in y-direction and confined in x-direction. The 2D-model was implemented in Excel as a finite difference model by using the built-in complex functions (click title to view).

  • Monte Carlo Scattering

    This is the representation of light propagation in a turbid medium. Light enters the medium from the left and changes its propagation direction due to scattering or is lost by absorption. The light propagation is modelled by a so-called Monte Carlo simulation, which simulates scattering and absorption by random processes mimicking the scattering and absorption probabilities quantified by the respective mean free paths. Grey-scale images can be used to visualize the scattering event densities in the medium (click title to view).

  • Fabry-Pérot Pressure Sensor

    The animation shows the response of a Fabry-Pérot (FP) sensor. The FP-cavity is comprised of a fixed and a deformable mirror that gives rise to a changing gap width as a function of pressure. This changes the spectral response of the FP-cavity, which modulates the spectrum of a white-light source. If this modulated light is probed by e.g. a Michelson-interferometer, the pressure dependent gap in the FP-cavity and hence the pressure can be retrieved. The animation shows the sensor gap, the FP-spectrum and the interferometer response (click title to view).

  • T.B.D.