000117693 001__ 117693
000117693 005__ 20240319081007.0
000117693 0247_ $$2doi$$a10.1364/OE.453952
000117693 0248_ $$2sideral$$a129184
000117693 037__ $$aART-2022-129184
000117693 041__ $$aeng
000117693 100__ $$0(orcid)0000-0001-6575-168X$$aGutiérrez Rodrigo, S.$$uUniversidad de Zaragoza
000117693 245__ $$aNeural network assisted design of plasmonic nanostructures on superconducting transition-edge-sensors for single photon detectors; 35472873
000117693 260__ $$c2022
000117693 5060_ $$aAccess copy available to the general public$$fUnrestricted
000117693 5203_ $$aTransition 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 (¿ = 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. © 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement Journal © 2022
000117693 536__ $$9info:eu-repo/grantAgreement/ES/MCIU/MAT2017-88358-C3-1-R$$9info:eu-repo/grantAgreement/ES/MCIU/PID2020-Q1115221GB-C41$$9info:eu-repo/grantAgreement/ES/MICINN/RTI2018-096686-B-C22
000117693 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000117693 590__ $$a3.8$$b2022
000117693 592__ $$a1.138$$b2022
000117693 591__ $$aOPTICS$$b30 / 99 = 0.303$$c2022$$dQ2$$eT1
000117693 593__ $$aAtomic and Molecular Physics, and Optics$$c2022$$dQ1
000117693 594__ $$a6.9$$b2022
000117693 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000117693 700__ $$0(orcid)0000-0002-5457-3694$$aPobes Aranda, C.
000117693 700__ $$aSánchez Casi, M.
000117693 700__ $$0(orcid)0000-0001-9273-8165$$aMartín-Moreno, L.
000117693 700__ $$0(orcid)0000-0001-7289-5649$$aCamón Lasheras, A.
000117693 7102_ $$12002$$2647$$aUniversidad de Zaragoza$$bDpto. Física Aplicada$$cÁrea Óptica
000117693 773__ $$g30, 8 (2022), 12368-12377$$pOpt. express$$tOPTICS EXPRESS$$x1094-4087
000117693 8564_ $$s6854252$$uhttps://zaguan.unizar.es/record/117693/files/texto_completo.pdf$$yVersión publicada
000117693 8564_ $$s2293182$$uhttps://zaguan.unizar.es/record/117693/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000117693 909CO $$ooai:zaguan.unizar.es:117693$$particulos$$pdriver
000117693 951__ $$a2024-03-18-14:47:45
000117693 980__ $$aARTICLE