We present a scalar model to overcome the computation time and sampling interval limitations of the traditional Rayleigh-Sommerfeld (RS) formula and angular spectrum method in computing wide-angle diffraction in the far-field. Numerical and experimental results show that our proposed method based on an accurate nonparaxial diffraction step onto a hemisphere and a projection onto a plane accurately predicts the observed nonparaxial far-field diffraction pattern, while its calculation time is much lower than the more rigorous RS integral. The results enable a fast and efficient way to compute far-field nonparaxial diffraction when the conventional Fraunhofer pattern fails to predict correctly.
We propose an iterative algorithm based on our scalar nonparaxial propagator for the design of Fourier diffractive optical elements (DOEs) having features on the order of the illumination wavelength. The simulation results show that our algorithm, using iterative Fourier transform and iterative projection, obtains higher-performance DOEs than a purely scalar paraxial design with the same order of calculation time. Upon verification with the experimental results, we find that our scalar-based design method is valid for DOEs with surprisingly small feature sizes (about half the wavelength) and diffraction angles up to about 37°.
In this article, we present the novel features of the recent version of SimAnMo, the Simulated Annealing Modeler. The tool creates models that correlate the size of one input parameter of an application to the corresponding runtime and thus SimAnMo allows predictions for larger input sizes. A focus lies on applications whose runtime grows exponentially in the input parameter size. Such programs are, for example, of high interest for cryptanalysis to analyze practical security of traditional and post-quantum secure schemes. However, SimAnMo also generates reliable models for the widespread case of polynomial runtime behavior and also for the important case of factorial runtime increase. SimAnMo's model generation is based on a parallelized simulated annealing procedure and heuristically minimizes the costs of a model. Those may rely on different quality metrics. Insights into SimAnMo's software design and its usage are provided. We demonstrate the quality of SimAnMo's models for different algorithms from various application fields. We show that our approach also works well on ARM architectures.
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