Electroplating Deposition of Bismuth Absorbers for X-ray Superconducting Transition Edge Sensors.

An absorber with a high absorbing efficiency is crucial for X-ray transition edge sensors (TESs) to realize high quantum efficiency and the best energy resolution. Semimetal Bismuth (Bi) has shown greater superiority than gold (Au) as the absorber due to the low specific heat capacity, which is two orders of magnitude smaller. The electroplating process of Bi films is investigated. The Bi grains show a polycrystalline rhombohedral structure, and the X-ray diffraction (XRD) patterns show a typical crystal orientation of (012). The average grain size becomes larger as the electroplating current density and the thickness increase, and the orientation of Bi grains changes as the temperature increases. The residual resistance ratio (RRR) (R300 K/R4.2 K) is 1.37 for the Bi film (862 nm) deposited with 9 mA/cm2 at 40 °C for 2 min. The absorptivity of the 5 µm thick Bi films is 40.3% and 30.7% for 10 keV and 15.6 keV X-ray radiation respectively, which shows that Bi films are a good candidate as the absorber of X-ray TESs.

An improved mixture robust probabilistic linear discriminant analyzer for fault classification.

This article introduces a novel fault classification method based on the mixture robust probabilistic linear discriminant analysis (MRPLDA). Unlike conventional probabilistic models like probabilistic principal component analysis (PPCA), probabilistic linear discriminant analysis (PLDA) introduces two sets of latent variables to represent the within-class and between-class information, resulting in an enhanced classification capability. In order to deal with outliers and non-Gaussian distributed variables commonly encountered in industrial processes, a mixture of robust PLDA model is considered by imposing the Student's t-priors on the noise and hidden variables of the PLDA model. Based on the model, a variational Bayesian expectation-maximization algorithm is developed for parameter estimation. In order to determine the state/class of a test sample, this article proposes a new state inference method by considering the joint probability between the test and training samples. The state inference method consists of a probability approximation, an evidence inference, and a voting based decision stage. The performance of the proposed fault classification method is illustrated by a numerical example and an application study to the Tennessee Eastman (TE) process.