Sensitivity Analysis and Topology Optimization in Plasmonics

Zhou Zeng, Purdue University

Abstract

The rapid development of topology optimization in photonics has greatly expanded the number of photonic structures with extraordinary performance. The optimization is usually solved by using a gradient-based optimization algorithm, where gradients are evaluated by the adjoint sensitivity analysis. While the adjoint sensitivity analysis has been demonstrated to provide reliable gradients for designs of dielectrics, there has not been too much success in plasmonics. The difficulty of obtaining accurate field solutions near sharp edges and corners in plasmonic structures, and the strong field enhancement jointly increase the numerical error of gradients, leading to failure of convergence for any gradient-based algorithm.We present a new method of calculating accurate sensitivity with the FDTD method by direct differentiation of the time-marching system in frequency domain. This new method supports general frequency-domain objective functions, does not relay on implementation details of the FDTD method, works with general isotropic materials and can be easily incorporated into both level-set-based and density-based topology optimizations. The method is demonstrated to have superior accuracy compared to the traditional continuous sensitivity. Next, we present a framework to carry out density-based topology optimization using our new sensitivity formula. We use the non-linear material interpolation to counter the rough landscape of plasmonics, adopt the filteringand-projection regularization to ensure manufacturability and perform the optimization with a continuation scheme to improve convergence.We give two examples involving reconstruction of near fields of plasmonic structures to illustrate the robustness of the sensitivity formula and the optimization framework. In the end, we apply our method to generate a rectangular temperature profile in the recording medium of the HAMR system.

Degree

M.Sc.

Advisors

Xu, Purdue University.

Subject Area

Artificial intelligence|Computer science|Design|Electromagnetics|Mathematics|Optics|Physics|Systems science

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