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Prober DE. Superconducting terahertz mixer using a transition-edge microbolometer. Appl Phys Lett. 1993;62(17):2119–21.
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Miller AJ, Lita AE, Calkins B, Vayshenker I, Gruber SM, Nam SW. Compact cryogenic self-aligning fiber-to-detector coupling with losses below one percent. Opt Express. 2011;19(10):9102–10.
Abstract: We present a compact packaging technique for coupling light from a single-mode telecommunication fiber to cryogenic single-photon sensitive devices. Our single-photon detectors are superconducting transition-edge sensors (TESs) with a collection area only a factor of a few larger than the area of the fiber core which presents significant challenges to low-loss fiber-to-detector coupling. The coupling method presented here has low loss, cryogenic compatibility, easy and reproducible assembly and low component cost. The system efficiency of the packaged single-photon counting detectors is verified by the “triplet method†of power-source calibration along with the “multiple attenuator†method that produces a calibrated single-photon flux. These calibration techniques, when used in combination with through-wafer imaging and fiber back-reflection measurements, give us confidence that we have achieved coupling losses below 1 % for all devices packaged according to the self-alignment method presented in this paper.
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Hoevers HFC, Bento AC, Bruijn MP, Gottardi L, Korevaar MAN, Mels WA, et al. Thermal fluctuation noise in a voltage biased superconducting transition edge thermometer. Appl Phys Lett. 2000;77(26):4421–4.
Abstract: The current noise at the output of a microcalorimeter with a voltage biased superconducting transition edge thermometer is studied in detail. In addition to the two well-known noise sources: thermal fluctuation noise from the heat link to the bath and Johnson noise from the resistive thermometer, a third noise source strongly correlated with the steepness of the thermometer is required to fit the measured noise spectra. Thermal fluctuation noise, originating in the thermometer itself, fully explains the additional noise. A simple model provides quantitative agreement between the observed and calculated noise spectra for all bias points in the superconducting transition.
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Galeazzi M. Fundamental noise processes in TES devices. IEEE Trans Appl Supercond. 2011;21(3):267–71.
Abstract: Microcalorimeters and bolometers are noise-limited devices, therefore, a proper understanding of all noise sources is essential to predict and interpret their performance. In this paper, I review the fundamental noise processes contributing to Transition Edge Sensor (TES) microcalorimeters and bolometers and their effect on device performance. In particular, I will start with a simple, monolithic device model, moving to a more complex one involving discrete components, to finally move to today's more realistic, comprehensive model. In addition to the basic noise contribution (equilibrium Johnson noise and phonon noise), TES are significantly affected by extra noise, which is commonly referred to as excess noise. Different fundamental processes have been proposed and investigated to explain the origin of this excess noise, in particular near equilibrium non-linear Johnson noise, flux-flow noise, and internal thermal fluctuation noise. Experimental evidence shows that all three processes are real and contribute, at different levels, to the TES noise, although different processes become important at different regimes. It is therefore time to discard the term “excess noise” and consider these terms part of the “fundamental noise processes” instead.
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Bennett DA, Schmidt DR, Swetz DS, Ullom JN. Phase-slip lines as a resistance mechanism in transition-edge sensors. Appl Phys Lett. 2014;104:042602.
Abstract: The fundamental mechanism of resistance in voltage-biased superconducting films is poorly understood despite its importance as the basis of transition-edge sensors (TESs). TESs are utilized in state-of-the-art microbolometers and microcalorimeters covering a wide range of energies and applications. We present a model for the resistance of a TES based on phase-slip lines (PSLs) and compare the model to data. One of the model's predictions, discrete changes in the number of PSLs, is a possible explanation for the observed switching between discrete current states in localized regions of bias.
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