Electromagnetic Theory E-18

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Thermo-Electrical EMC Filter Design for Electric Vehicles
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Roboter-Nahfeldmessung unter Verwendung der Einzelsonden-OTF-Technik
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Active Learning for Optimization of EMC processes
With the ever increasing operating frequencies and powers, EMC has now become a major consideration on any project involving the design, construction, manufacture and installation of electrical and electronic equipment and systems. An important step in the design of components and prediction of EMC related problems is the modeling and simulation. The complexity and performance of electrical and electronic devices as well as the number and range of variables in the design spaces means that many of the Physics-Based (PB) used tools are either too slow or too inaccurate for effective design and optimization. Recently, machine learning (ML) tools and techniques have been increasingly used in the EMC domain either to improve PB approaches or to replace them. In ML, computers are used to probe vast amounts of data for structure. One requirement for the effective ML model building is the availability of these large datasets for the training and testing processes. The generation of simulation data of EMC systems using PB tools is more than often quite expensive and very time consuming. The main goal of this project is the adaptation and extension of active learning schemes such as Bayesian Optimization (BO) to the realm of EMC. In active learning, the expensive to evaluate samples used for training and prediction are intelligently collected at each iteration to reduce the needed number of simulation runs. The active learning methods can be used for optimization tasks, modeling of systems or both at the same time. The main working points can be summarized as: - Adaptation and foundation-laying of BO-based optimization and model building in the fields of EMC, SI, PI and bio-EMC. - Establishment of a framework to evaluate the certainty in the solutions provided by the active learning scheme. - Contribution to the available databases with the generated datasets for the service of the electrical engineering and EMC community.
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Daten Getriebener Entwurf und Analyse von Hochgeschwindigkeits Verbindungen of Leiterplatten
The expansion of information networks such as the industrial Internet of Things (IoT) is creating an ever-increasing demand for data. This logically generates a demand for data generation, processing, storage and data transport between these steps. This project is investigating ways to ensure fast data transport. The goal is to model “Ultra-High-Speed Interconnects” that enable data transmission of more than 100 Gbps per data link. These links usually consist of two components. One is the physical channel and the other is an I/O structure. In addition, challenges arise in the form of losses due to the materials used and other disturbances that must also be taken into account. Among other things, the influence of different equalization will be investigated and adapted to physical models of the channel. For this purpose, it is planned to use machine learning methods, e.g. to be able to make predictions about possible design parameters for these Ultra-High-Speed Interconnects.
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MLE Summer School 2022
On September, 13th and 14th, 2022 the first Summer School for Machine Learning in Engineering took place on the campus of the Hamburg University of Technology (TUHH). The school was organized by the Machine Learning in Engineering (MLE) research initiative of TUHH in collaboration with the Helmholtz Center Hereon, the Deutsches Elektronen-Synchrotron DESY, DASHH, CDCS, ARIC, ahoi.digital, the Career Center of TUHH and the AI.Startup.Hub. With over 100 participating master students, PhD students and postdocs, the school met with an extremely positive response and was a complete success. The participants, mostly from the greater Hamburg area but also from all over Germany, were enthusiastic about the offer and the organization. The two days offered participants a diverse program with insights into the world of machine learning (ML). The program was fully organized by members of the MLE Research Initiative.
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MLE Days
Maschinelles Lernen ist zu einem zentralen Treiber wirtschaftlicher Innovation und Entwicklung geworden. Das Anwendungspotenzial in den Ingenieurwissenschaften ist aber vergleichsweise wenig bekannt und ausgeschöpft. Die MLE Initiative verfolgt unter anderem das Ziel des Wissenstransfers in Richtung Wirtschaft und Industrie. Dazu bieten wir regelmäßig Workshops zu einschlägigen Themen an. Die Themen decken sowohl die Grundlagen des Maschinellen Lernens als auch spezielle Anwendungen in den Ingenieurwissenschaften ab – vom Maschinenbau, Elektro‑ und Informationstechnik, Materialwissenschaften bis hin zu Betriebswirtschaft und Logistik.
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Machine Learning Methods for Application in Bio-EMC
Bio-electromagnetic (BioEM) engineering, an emerging inter-discipline area with many high-end technologies involved, draws tremendous interest of researchers and scientists. It is foreseeable that human implants will be developed as highly integrated and intelligent devices of ultra-small size, low-power consumption but full abilities of sensing, controlling, data processing, wireless data and power transmission. Among them, one of the most challenging designs is the human brain implant, which is expected to change patients’ life as well as promote normal human beings’ life. Since brain implants are in a very close interaction with a human body and complicated brain tissues, it is essential to learn and to control their EM effects on a human brain environment. This requires using highly accurate, efficient and reliable modelling, simulation and measurement tools and techniques to characterize their hierarchical, anisotropic, dispersive features. Furthermore, crosstalk and interference between implants and on-/off-body electronics causes more issues in realistic scenarios. Considering all above, this project aims to develop brain implants and human head models, improve simulation methods and facilitate measurement and validation ways to get closer to real state in this principal criterion. Especially, machine learning (ML) methods, which has witnessed a rapid development in solving complex electromagnetic (EM) problems, will be adapted to solve Bio-EMC problems for brain implants, and three work packages are planned as following: Getting realistic implant model with wireless power transfer and realistic human head model are the main primary step to get in to the problem. Hence, in this work package, the focus is mainly providing accurate simulation models for both of them. Near Field full-wave simulation provides the most accurate simulation results, but it is obviously time and memory consuming due to huge numerical calculations. Adaptive sampling and ML-based clustering and data acquisition of Huygens surface is expected to be a replacement for full wave simulations. Hence, in this work package, an adaptive sampling method will be used to detect brain implant EM field on human head models and errors in comparison to full wave simulation will be investigated. ML-based Source reconstruction and EM inverse scattering is another interest in this work package which will be used in other parts for optimization as well. Measurements using Near-Field scanner for simulation result validation will be done. Elaborating the problem to DUT-to-DUT correlation of human head model using several brain implants as well as applying Machine Learning Methods for EMC analysis regarding various parameters, such as the position, the distance, the orientation and the frequency is the main interest in this work package.
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Electronic Design Flow Improvement with Machine Learning Tools
The design of modern printed circuit boards (PCBs) is a challenging task and requires the compliance with a variety of specifications. To reduce the risk of a poor design and getting the desired functionality, the design processs is accompanied by many electromagnetic simulations. In combination with the time requirement of an individual simulaion this results in a large effort which needs a lot of computational resources. Because future designs will have a higher complexity and larger integration level, decreasing the time requirement for an individual simulation is an important task to solve. Without improving the simulation mechanism the design ow will require more time which results in additional costs for the development. This project aims to improve the design ow by providing a more efficient design tool and process. In the area of machine learning algorithms some promising ideas are found. Publications in recent years provide methods to increase the efficiency of optimization processes – for example generic algorithms for the placement of decoupling capacitors. With artificial neural networks first results are achieved by investigating the impedance of the power delivery network under the in uence of decoupling capacitors. Future work shall increase the applicability of artificial neural networks to a wider range of simulation tasks. Therefore different aspects have to be investigated. The focus is on printed circuit boards which are commonly used in many electronic devices. The functionality and capabilities of printed circuit boards is well understood. One of the most important aspects within this project is to provide not only a faster simulation ow but to ensure the consistency of simulation results with existing tools and methods.
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High Speed Automotive Interconnect Design for Gbps Links
High-speed interconnect design for wired links in digital systems has been a well-established field of research and innovation for several decades now. In contrast, automotive high-speed interconnects have only recently advanced to higher and higher data rates. Correspondingly, models for components specific to automotive interconnects have not been created or validated to a large extent nor has a sufficient model-to-hardware correlation been established. In addition, automotive high-speed interconnects must fulfill strict requirements regarding electromagnetic compatibility (EMC) regulations. This project aims to develop high-speed interconnect models used in automotive applications on a system level. These models will be adapted to analyze printed circuit board (PCBs) stack-ups used in automotive applications in combination with assembly and interconnection technologies (AITs) and provide insight of the Signal Integrity performance of high-speed links in harsh automotive environmental standards to have a functional system which fulfill the electrical performance as well as reliability requirements.
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Fast Simulations of Energy Selective Surfaces via Reduced Order Models
Since the 1970s, when the starting point was a single wire antenna loaded with a diode, the simulation of electrical/electromagnetic structures with nonlinear terminations is major topic of interest in the academia. Examples are active antennas, grid amplifiers, amplifier with shielding structures, on-chip interconnects with NL terminations, multistage amplifiers, and PCB decoupling problems. The general trend over the years shows dynamical systems of increasing size, massively loaded with NL components. The aim of this project is improving the state-of-the-art in numerical simulation of large electrical/electromagnetic structures massively loaded with NL components/terminations. Examples are meta-surfaces for nonlinear shielding, or power delivery networks in Printed Circuit Boards (PCBs). The interplay between distributed (electrically large) nature and presence of NL terminations make the numerical simulation problem (to be cast in time-domain) extremely challenging. Our main effort will be dedicated to address two major limitations of state-of-the-art mixed circuit/field simulation methods, with specific reference to approaches based on model-order-reduction (macromodeling): Improve and streamline generation of compact, passive and reliable macromodels for large-scale components, with hundreds of ports, obtained from tabular frequency responses Systematic extraction and inclusion of reduced-order models also for the nonlinear terminations in a dedicated simulation environment, when the number of NL loads becomes very large Since 2018, the groups of TUHH and POLITO have been working together at the project “Hybrid Simulation of Electromagnetic Field Interaction with Metallic Structures Showing Massive Nonlinear Loading”, supported by DFG. The ongoing collaboration is aimed to advance the state-of-the-art in the simulation of field interactions with nonlinear loaded structure, with a focus on meta-surfaces (metallic grids connected with diodes). The project involves a Ph.D. student from TUHH, M.Sc. T. Wendt, with special care to the interface between the home-made MoM solver provided by TUHH and the macromodeling generation part.
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