Neural network assisted design of plasmonic nanostructures on superconducting transition-edge-sensors for single photon detectors

Abstract

Transition edge sensors (TESs) are extremely sensitive thermometers made of superconducting materials operating at their transition temperature, where small variations in temperature give rise to a measurable increase in electrical resistance. Coupled to suitable absorbers, they are used as radiation detectors with very good energy resolution in several experiments. Particularly interesting are the applications that TESs may bring to single photon detection in the visible and infrared regimes. In this work, we propose a method to enhance absorption efficiency at these wavelengths. The operation principle exploits the generation of highly absorbing plasmons on the metallic surface. Following this approach, we report nanostructures featuring theoretical values of absorption reaching 98%, at the telecom design frequency (λ = 1550 nm). The optimization process takes into account the TES requirements in terms of heat capacity, critical temperature and energy resolution leading to a promising design for an operating device. Neural networks were first trained and then used as solvers of the optical properties of the nanostructures. The neural network topology takes the geometrical parameters, the properties of materials and the wavelength of light as input, predicting the absorption spectrum at single wavelength as output. The incorporation of the material properties and the dependence with frequency was crucial to reduce the number of required spectra for training. The results are almost indistinguishable from those calculated with a commonly used numerical method in computational electromagnetism, the finite-difference time-domain algorithm, but up to 106 times faster than the numerical simulation.