Neural modeling and space mapping: two approaches to circuit design

The drive in the microwave industry for manufacturability-driven design and time-to-market demands powerful and efficient computer-aided design tools. The need for statistical analysis and yield optimization coupled with the desire to use accurate physics-based and EM-based models leads to tasks that are computationally intensive using conventional approaches. We present two recent advances in the microwave CAD area, Artificial Neural Network (ANN) based modeling and Space Mapping (SM) based modeling for fast and accurate design of microwave components and circuits.Consejo Nacional de Ciencia y TecnologíaCarleton Universit

Solar Neutrinos: Radiative Corrections in Neutrino-Electron Scattering Experiments

Radiative corrections to the electron recoil-energy spectra and to total cross sections are computed for neutrino-electron scattering by solar neutrinos. Radiative corrections change monotonically the electron recoil spectrum for incident \b8 neutrinos, with the relative probability of observing recoil electrons being reduced by about 4 \% at the highest electron energies. For $p-p$ and \be7 neutrinos, the recoil spectra are not affected significantly. Total cross sections for solar neutrino-electron scattering are reduced by about 2 \% compared to previously computed values. We also calculate the recoil spectra from $^{13}$N and $^{15}$O neutrinos including radiative corrections.Comment: 40 pages, uuencoded, Z-compress file

Software implementation of space mapping based neuromodels of microwave components

We present novel realizations of SM based neuromodels of microwave components using available software. In the SM based neuromodeling techniques a neural network is used to implement a mapping from the electromagnetic to the circuit-theoretic input space. The implicit knowledge in the circuit model allows us to decrease not only the number of learning points needed, but also the complexity of the neural network and to improve the generalization performance. A Frequency Space Mapped Neuromodel (FSMN) of a microstrip right angle bend is implemented using NeuroModeler, and entered into ADS as a library component through an ADS plug-in module.Consejo Nacional de Ciencia y TecnologíaCarleton Universit

Proximity Effects and Nonequilibrium Superconductivity in Transition-Edge Sensors

We have recently shown that normal-metal/superconductor (N/S) bilayer TESs (superconducting Transition-Edge Sensors) exhibit weak-link behavior.1 Here we extend our understanding to include TESs with added noise-mitigating normal-metal structures (N structures). We find TESs with added Au structures also exhibit weak-link behavior as evidenced by exponential temperature dependence of the critical current and Josephson-like oscillations of the critical current with applied magnetic field. We explain our results in terms of an effect converse to the longitudinal proximity effect (LoPE)1, the lateral inverse proximity effect (LaiPE), for which the order parameter in the N/S bilayer is reduced due to the neighboring N structures. Resistance and critical current measurements are presented as a function of temperature and magnetic field taken on square Mo/Au bilayer TESs with lengths ranging from 8 to 130 {\mu}m with and without added N structures. We observe the inverse proximity effect on the bilayer over in-plane distances many tens of microns and find the transition shifts to lower temperatures scale approximately as the inverse square of the in- plane N-structure separation distance, without appreciable broadening of the transition width. We also present evidence for nonequilbrium superconductivity and estimate a quasiparticle lifetime of 1.8 \times 10-10 s for the bilayer. The LoPE model is also used to explain the increased conductivity at temperatures above the bilayer's steep resistive transition.Comment: 10 pages, 8 figure

Realizations of Space Mapping based neuromodels of microwave components

Artificial Neural Networks (ANN) are suitable in modeling high-dimensional and highly nonlinear elements, such as those found in the microwave arena. In modeling microwave components, the learning data is obtained from a detailed or “fine” model (typically an EM simulator), which is accurate but slow to evaluate. This is aggravated because simulations are needed for many combinations of input parameter values. This is the main drawback of conventional ANN modeling. We use available equivalent circuits or “coarse” models to overcome this limitation. In the Space Mapping (SM) based neuromodeling techniques an ANN is used to implement a suitable mapping from the fine to the coarse input space. The implicit knowledge in the coarse model not only allows us to decrease significantly the number of learning points needed, but also to reduce the complexity of the ANN and to improve the generalization performance. We present novel realizations of SM based neuromodels of practical passive components using commercial software. An SM-based neuromodel of a microstrip right angle bend is developed using NeuroModeler, and entered into HP ADS as a library component through an ADS plug-in module.Consejo Nacional de Ciencia y TecnologíaCom DevNSER

Magnetic Calorimeter Option for the Lynx X-Ray Microcalorimeter

One option for the detector technology to implement the Lynx x-ray microcalorimeter (LXM) focal plane arrays is the metallic magnetic calorimeter (MMC). Two-dimensional imaging arrays of MMCs measure the energy of x-ray photons by using a paramagnetic sensor to detect the temperature rise in a microfabricated x-ray absorber. While small arrays of MMCs have previously been demonstrated that have energy resolution better than the 3 eV requirement for LXM, we describe LXM prototype MMC arrays that have 55,800 x-ray pixels, thermally linked to 5688 sensors in hydra configurations, and that have sensor inductance increased to avoid signal loss from the stray inductance in the large-scale arrays when the detectors are read out with microwave superconducting quantum interference device multiplexers, and that use multilevel planarized superconducting wiring to provide low-inductance, low-crosstalk connections to each pixel. We describe the features of recently tested MMC prototype devices and simulations of expected performance in designs opti- mized for the three subarray types in LXM

BUMC Annual Report

Annual report of the Boston University Medical Center

Neural space mapping optimization for EM-based design of RF and microwave circuits

We review Neural Space Mapping (NSM) optimization for electromagnetic-based design of RF and microwave circuits. NSM optimization exploits our Space Mapping-based neuromodeling techniques to efficiently approximate a suitable mapping at each iteration. Coarse model sensitivities are exploited to select suitable fine model base points for the initial mapping.Bandler CorporationConsejo Nacional de Ciencia y Tecnologí