The bilayers of Transition Edge Sensors (TESs) are often modified with additional normal-metal features such as bars or dots. Previous device measurements suggest that these features improve performance, reducing electrical noise and altering response times. However, there is currently no numerical model to predict and quantify these effects. Here we extend existing techniques based on Usadel's equations to describe TESs with normal-metal features. We show their influence on the principal TES characteristics, such as the small-signal electrothermal parameters α and β and the superconducting transition temperature Tc. Additionally, we examine the effects of an applied magnetic field on the device performance. Our model predicts a decrease in Tc, α and β as the number of lateral metal structures is increased. We also obtain a relationship between the length L of a TES and its critical temperature, Tc ∝ L −0.7 for a bilayer with normal-metal bars. We predict a periodic magnetic flux dependence of α, β and Ic. Our results demonstrate good agreement with published experimental data, which also show the reduction of α, β and Tc with increasing number of bars. The observed Fraunhofer dependence of critical current on magnetic flux is also anticipated by our model. The success of this model in predicting the effects of additional structures suggests that in the future numerical methods can be used to better inform the design of TESs, prior to device processing.

Transition Edge Sensors are ultra-sensitive superconducting detectors with applications in many areas of research, including astrophysics. The device consists of a superconducting thin film, often with additional normal metal features, held close to its transition temperature and connected to two superconducting leads of a higher transition temperature. There is currently no way to reliably assess the performance of a particular device geometry or material composition without making and testing the device. We have developed a proximity effect model based on the Usadel equations to predict the effects of device geometry and material composition on sensor performance. The model is successful in reproducing I − V curves for two devices currently under study. We use the model to suggest the optimal size and geometry for TESs, considering how small the devices can be made before their performance is compromised. In the future, device modelling prior to manufacture will reduce the need for time-consuming and expensive testing.

This paper examines the kinematics of parallel linkage arrangement referred to as a tripod. The mechanism has 6 degrees of freedom and is isomorphic to a 6-3 Stewart platform. A practical and fast forward kinematics solution is given, based on mapping to the 6-3 Stewart platform, and has been implemented on a prototype platform in both Python and Matlab. The platform is designed for a set of Kirkpatrick-Baez X-ray mirrors and the results show that an accuracy of the order of tens of nanometres is possible.

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