Instrumentation for energetic Neutral atom measurements at Mars, Venus and The Earth

This thesis deals with the development and calibrations of sensors to measure energetic neutral atoms (ENAs) at Mars, Venus, and the Earth. ENAs are formed in charge exchange processes between energetic, singly--charged ions and a cold neutral gas. Since ENAs can travel in long straight trajectories, unaffected by electric or magnetic fields, they can be used to remotely image plasma interactions with neutral atmospheres. ENA instrument techniques have matured over the last decade and ENA images of the Earth's ring current for example, have successfully been analyzed to extract ion distributions and characterize plasma flows and currents in the inner magnetosphere. Three different ENA sensors have been developed to image ENAs at Mars, Venus, and the Earth. Two of them, the nearly identical Neutral Particle imagers (NPIs) are on-board the Mars Express and Venus Express spacecraft as a part of the Analyzer of Space Plasmas and Energetic Atoms (ASPERA-3 and 4) instruments. The third is the Neutral Atom Detector Unit, NUADU, aboard the TC-2 spacecraft of the Double Star mission. The NPI design is based on a surface reflection technique to measure low energy (~0.3-60 keV) ENAs, while the NUADU instrument is based on a simple design with large geometrical factor and solid state detectors to measure high energy ENAs (~20-300 keV). The calibration approach of both NPI sensors were to define the detailed response, including properties such as the angular response function and efficiency of one reference sensor direction then find the relative response of the other sensor directions. Because of the simple geometry of the NUADU instrument, the calibration strategy involved simulations to find the cutoff energy, geometrical factor and angular response. The NUADU sensor head was then calibrated to find the response to particles of different mass and energy. The NPI sensor for the Mars Express mission revealed a so-called priority effect in the sensor that lowers the angular resolution at high detector bias. During the calibration of the Venus Express NPI sensor tests were made which showed that the priority effect is a result of low amplitude (noise) pulses generated in the detector system. The conclusion is that the effect is caused by capacitive couplings between different anode sectors of the sensor. The thresholds on the preamplifiers were set higher on the Venus Express NPI, which removed the priority effect. Two of the three ENA experiments, the Double Star NUADU instrument and the Mars Express NPI sensor, have successfully measured ENAs that are briefly described in the thesis. The first ENA measurements at Mars were performed with Mars Express. Initial results from the NPI include measurements of ENAs formed in the Martian magnetosheath and solar wind ENAs penetrating to the nightside of Mars. The first results from NUADU in Earth orbit show the expected ENA emissions from a storm time ring current. Also, together with the HENA instrument on the IMAGE spacecraft, NUADU have produced the first multi-point ENA image of the ring current

Data-Driven Remaining Useful Life Estimation of Discrete Power Electronic Devices

Robust and accurate prognostics models for estimation of remaining useful life (RUL) are becoming an increasingly important aspect of research in reliability and safety in modern electronic components and systems. In this work, a data driven approach to the prognostics problem is presented. In particular, machine learning models are trained to predict the RUL of wire-bonded silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) subjected to power cycling until failure. During such power cycling, ON-state voltage and various temperature measurements are continuously collected. As the data set contains full run-to-failure trajectories, the issue of estimating RUL is naturally formulated in terms of supervised learning. Three neural network architectures were trained, evaluated, and compared on the RUL problem: a temporal convolutional neural network (TCN), a long short-term memory neural network (LSTM) and a convolutional gated recurrent neural network (Conv-GRU). While the results show that all networks perform well on held out testing data if the testing samples are of similar aging acceleration as the samples in the training data set, performance on out-of-distribution data is significantly lower. To this end, we discuss potential research directions to improve model performance in such scenarios.ch is conducted within the iRel4.0 Intelligent Reliability project, which is funded by Horizon2020 Electronics Components for European LeadershipJoint Undertaking Innovation Action (H2020-ECSELJU-IA). This work is also funded by the Swedish innovation agency Vinnova, through co-funding of H2020-ECSEL-JU-IA.iRel4.

Effect of PCB cracks on thermal cycling reliability of passive microelectronic components with single-grained solder joints

Lead-free tin-based solder joints often have a single-grained structure with random orientation and highly anisotropic properties. These alloys are typically stiffer than lead-based solders, hence transfer more stress to printed circuit boards (PCBs) during thermal cycling. This may lead to cracking of the PCB laminate close to the solder joints, which could increase the PCB flexibility, alleviate strain on the solder joints, and thereby enhance the solder fatigue life. If this happens during accelerated thermal cycling it may result in overestimating the lifetime of solder joints in field conditions. In this study, the grain structure of SAC305 solder joints connecting ceramic resistors to PCBs was studied using polarized light microscopy and was found to be mostly single-grained. After thermal cycling, cracks were observed in the PCB under the solder joints. These cracks were likely formed at the early stages of thermal cycling prior to damage initiation in the solder. A finite element model incorporating temperature-dependant anisotropic thermal and mechanical properties of single-grained solder joints is developed to study these observations in detail. The model is able to predict the location of damage initiation in the PCB and the solder joints of ceramic resistors with reasonable accuracy. It also shows that the PCB cracks of even very small lengths may significantly reduce accumulated creep strain and creep work in the solder joints. The proposed model is also able to evaluate the influence of solder anisotropy on damage evolution in the neighbouring (opposite) solder joints of a ceramic resistor.Funding details: VINNOVA, 2015-01420; Funding details: Swedish Insitute, SI; Funding text 1: This work has been conducted within the Swedish national project "Requirements, specification and verification of environmental protection and life of solder joints to components" supported by the Swedish Governmental Agency for Innovation Systems (Vinnova) under contract 2015-01420 .</p

Data-Driven Remaining Useful Life Estimation of Discrete Power Electronic Devices

Robust and accurate prognostics models for estimation of remaining useful life (RUL) are becoming an increasingly important aspect of research in reliability and safety in modern electronic components and systems. In this work, a data driven approach to the prognostics problem is presented. In particular, machine learning models are trained to predict the RUL of wire-bonded silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFETs) subjected to power cycling until failure. During such power cycling, ON-state voltage and various temperature measurements are continuously collected. As the data set contains full run-to-failure trajectories, the issue of estimating RUL is naturally formulated in terms of supervised learning. Three neural network architectures were trained, evaluated, and compared on the RUL problem: a temporal convolutional neural network (TCN), a long short-term memory neural network (LSTM) and a convolutional gated recurrent neural network (Conv-GRU). While the results show that all networks perform well on held out testing data if the testing samples are of similar aging acceleration as the samples in the training data set, performance on out-of-distribution data is significantly lower. To this end, we discuss potential research directions to improve model performance in such scenarios.ch is conducted within the iRel4.0 Intelligent Reliability project, which is funded by Horizon2020 Electronics Components for European LeadershipJoint Undertaking Innovation Action (H2020-ECSELJU-IA). This work is also funded by the Swedish innovation agency Vinnova, through co-funding of H2020-ECSEL-JU-IA.iRel4.

Development of Prototype Low-Cost QTSS™ Wearable Flexible More Enviro-Friendly Pressure, Shear, and Friction Sensors for Dynamic Prosthetic Fit Monitoring

There is a current healthcare need for improved prosthetic socket fit provision for the masses using low-cost and simple to manufacture sensors that can measure pressure, shear, and friction. There is also a need to address society’s increasing concerns regarding the environmental impact of electronics and IoT devices. Prototype thin, low-cost, and low-weight pressure, shear, and loss of friction sensors have been developed and assembled for trans-femoral amputees. These flexible and conformable sensors are simple to manufacture and utilize more enviro-friendly novel magnetite-based QTSS™ (Quantum Technology Supersensor™) quantum materials. They have undergone some initial tests on flat and curved surfaces in a pilot amputee trial, which are presented in this paper. These initial findings indicate that the prototype pressure sensor strip is capable of measuring pressure both on flat and curved socket surfaces in a pilot amputee trial. They have also demonstrated that the prototype shear sensor can indicate increasing shear forces, the resultant direction of the shear forces, and loss of friction/slippage events. Further testing, amputee trials, and ongoing optimization is continuing as part of the SocketSense project to assist prosthetic comfort and fit