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Reinhard Caspary

Publications and source records attributed to Reinhard Caspary.

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AMELI: Angular Matrix Elements of Lanthanide Ions

Matrix elements of spherical tensor operators are fundamental to analyzing lanthanide spectra in both amorphous and crystalline host materials. This work presents a comprehensive framework for calculating angular matrix elements using a Slater determinant basis and their subsequent transformation to the traditional $LS$-coupling scheme using the classification introduced by Racah. While computationally demanding, this direct product-state approach is more universally applicable than conventional methods and remains well within modern desktop computing capabilities. We provide a concise set of general rules to calculate angular matrix elements for virtually any spherical tensor operator within an $f^N$ configuration. Because these matrices are mathematical constants independent of the host environment, they need only be calculated once. A comprehensive set of calculated matrix elements for unit and angular momentum operators, alongside perturbation Hamiltonians, is made available in the open-access repository AMELI. By utilizing exact arithmetic, AMELI eliminates the numerical artifacts and rounding errors inherent to conventional floating-point representations. This takes full advantage of the selection rules and symmetry properties of each operator, resulting in a very compact data format due to the high sparsity of the matrices and the small number of unique non-zero elements. While the evaluation of final physical observables requires subsequent numerical diagonalization, this foundational repository is intended to replace legacy tables currently used for semi-empirical calculations. Extensive quantitative comparisons to classic tables from Judd and Carnall are presented, and application examples are demonstrated using the open-source Python reference implementation YALIP.

physics.comp-ph

A flexible framework for large-scale FDTD simulations: open-source inverse design for 3D nanostructures

We introduce an efficient open-source python package for the inverse design of three-dimensional photonic nanostructures using the Finite-Difference Time-Domain (FDTD) method. Leveraging a flexible reverse-mode automatic differentiation implementation, our software enables gradient-based optimization over large simulation volumes. Gradient computation is implemented within the JAX framework and based on the property of time reversibility in Maxwell's equations. This approach significantly reduces computational time and memory requirements compared to traditional FDTD methods. Gradient-based optimization facilitates the automatic creation of intricate three-dimensional structures with millions of design parameters, which would be infeasible to design manually. We demonstrate the scalability of the solver from single to multiple GPUs through several inverse design examples, highlighting its robustness and performance in large-scale photonic simulations. In addition, the package features an object-oriented and user-friendly API that simplifies the specification of materials, sources, and constraints. Specifically, it allows for intuitive positioning and sizing of objects in absolute or relative coordinates within the simulation scene. By rapid specification of the desired design properties and rapid optimization within the given user constraints, this open-source framework aims to accelerate innovation in photonic inverse design. It yields a powerful and accessible computational tool for researchers, applicable in a wide range of use cases, including but not limited to photonic waveguides, active devices, and photonic integrated circuits.

physics.optics

Quantized Inverse Design for Photonic Integrated Circuits

The inverse design of photonic integrated circuits (PICs) presents distinctive computational challenges, including their large memory requirements. Advancements in the two-photon polymerization (2PP) fabrication process introduce additional complexity, necessitating the development of more flexible optimization algorithms to enable the creation of multi-material 3D structures with unique properties. This paper presents a memory efficient reverse-mode automatic differentiation framework for finite-difference time-domain (FDTD) simulations that is able to handle complex constraints arising from novel fabrication methods. Our method is based on straight-through gradient estimation that enables non-differentiable shape parametrizations. We demonstrate the effectiveness of our approach by creating increasingly complex structures to solve the coupling problem in PICs. The results highlight the potential of our method for future PIC design and practical applications.

physics.optics