Demonizing the Aggressors: Russian Icons of <i>The Battle of Novgorodians against Suzdalians</i>

Antonov, Dmitriy I.

doi:10.1484/j.ikon.5.132353

Cited by 1 publication

(2 citation statements)

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“…As shown by the results obtained in the framework of this work, the proposed techniques can significantly improve the efficiency of the algorithm described in [3] and [8]. Also, these techniques can be used to optimize software products created on the basis of the original multiplate methods of RCWA (for example, [7] and [14]), giving a full solution, that is, determining the DE of both transmitted and reflected diffraction orders.…”

Section: Discussionmentioning

confidence: 80%

“…where εĩ is the i-th Fourier coefficient, i min and i max are the minimum and maximum values of the number of diffraction orders taken into account (the case with one-dimensional periodicity is shown). For example, the algorithm proposed in [8] calculates each element of the matrix E separately. However, the matrix E is a Toeplitz matrix (see, for example, article [9]), that is, it has repeating elements parallel to the main diagonal.…”

Section: Technique №mentioning

confidence: 99%

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Techniques for speeding up algorithms for implementing rigorous coupled-wave analysis

Antonov,

Greisukh,

Ezhov

2024

International Academic Conference on Optics and Photonics (IACOP 2023)

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This paper proposes three techniques to reduce the time required to calculate single diffraction efficiencies or a series of diffraction efficiencies obtained from rigorous coupled-wave analysis algorithms. These are a technique using the properties of the Toeplitz matrix, a technique for reducing the number of variables in a function of Fourier coefficients, and a technique for using parallel computing. On the example of tasks on plotting the dependences of the diffraction efficiency of two-layer two-relief sawtooth microstructures with antireflection coatings on the angle of incidence of radiation, it is shown that using the properties of Toeplitz matrices can significantly reduce the calculation time. Parallel computing also reduces the calculation time, but it uses more RAM.

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