Speakers

Over the past few decades, electromagnetics has created a revolution in the way engineering is applied resulting in numerous new products across many areas of consumer and industrial applications.  Although the underlying physics and principles have been established for over a century, the availability of electronic components, low cost processing power, increased bandwidths and computer simulation tools have produced new capabilities and technologies that would have been impossible or impractical just a few years ago.

 This talk will look forward to the opportunities for electromagnetic sensing, when harnessing and integrating techniques across spectrum from DC to microwaves, so called hyper wideband for new applications.  The talk will highlight some recent research at Manchester in collaboration with the workshop organisers at the University of Zagreb.

In eddy current nondestructive testing (NDT), models and simulation are invaluable tools for understanding the underlying phenomena, for design of probes and inspection procedures, for interpretation of results, for evaluation of POD curves and for training and education. In general, the terms simulation and modeling are used interchangeably. For distinction purposes modeling refers to the act of building a model which in turn refers to a mathematical/software product that can be used to simulate a system. Simulation refers to the process of using a model to study the behavior and performance of the system. Modeling of eddy current NDT configurations can be categorized as analytical, semi-analytical and numerical and is based on the efficient solution of Maxwell's equations in the so-called quasistatic limit, either by differential or integral methods. The former imply Finite Element Method models used in general purpose commercial software while the latter are preferred in existing NDT-dedicated software. In this presentation, specific eddy current NDT configurations are simulated by using a number of models. Progress in the development of such models is discussed together with ideas and plans for their extension.

Technology of ultraprecise speed and distance radar measurements using microwave radars will be presented and demonstrated. Measurements of speed with accuracy better than 1 mm/s and measurement of distance with resolution of 10 μm in the measurement range from 0.2 m to 10 m distance from the radar can be achieved with modern radar technology, incorporating clever microwave frontend design, smart antenna pattern design, and lot of signal processing in the digital processing unit of the radar. Challenges in the design of such devices, techniques and equipment used in R&D process for such devices and later certification, and some real-world applications will be presented and discussed. 

The non-invasive evaluation of organic matter is a major challenge in many fields such as agriculture, food engineering and healthcare. Among the low-cost sensing techniques, bio-impedance, capacitive or inductive measurements are good candidates to assess such complex matter through the sensing of their dielectric properties. Indeed, the complex permittivity is determined by and informative about the nature, the composition and the state of organic materials. It is considered as relevant indicator of physicochemical or physiopathological changes within the organic matter. In this presentation, we will focus on transmission-line based LC resonators, used as passive and inductive sensors for non-contact monitoring applications. More specifically, an original wireless multifrequency transmission-line resonator (WMFR) sensor will be presented. Associated with a multi-frequency equivalent electrical model, the WMFR allows to distantly monitor the changes of the complex permittivity of the investigated matter on a given set of frequencies, and thus to produce a multiscale analysis, spatially and / or over time. Experimental validations of the approach are carried out using different study supports, such as dairy products, ripening fruits, tissue decomposition, and localization of local dielectric contrasts. Such sensors open the way to the development of noninvasive and low-cost devices for 3D or 4D multiscale monitoring of organic matter.

Current robot systems are limited in their abilities of safe physical interaction with humans and objects in the real world. Tasks performed by robots are still rather slow, rather simple, and not optimal as it is often necessary to isolate robots (e.g. behind fences) to avoid collisions. This makes these robots not suitable for user needs where they are expected to add value in several application areas. There is a need to extend robots with the abilities of safe and effective interaction with humans and objects in their environment. One of the reasons for today’s robots‘ limitations is the missing ability to comprehensively perceive the environment in real-time. Such abilities are fundamental for interaction with humans and the world and for task specific adaptation of actions based on such interaction and experience. In order to address these topics, sensors for the perception of the environment which are co-designed with specific integrated circuits are core elements. Consequently, the scientific and technological objective of PATTERN-Skin is to develop a novel embodied bendable and potentially stretchable multimodal modular robot skin that provides robots with unprecedented sensing abilities facilitating contact-based/tactile and contact-less multimodal exploration of the world towards safe human-robot interaction. Besides the physical realization of the skin modules, also physically accurate real time simulations (“digital twin”) are developed that allow to optimize and tailor skin configurations for robots and applications. Based on this sensor skin and the corresponding digital twin, PATTERN-Skin uses model based and AI-based methods to obtain representations of the environment towards the utilization in safe control strategies and aiming to meet requirements as defined e.g. in the ISO 15066 and 10218 safety standards. With respect to safe, reliable, and secure assembly of full systems from a number of individual sensor skin modules, a unified design pattern utilizing Near Field Communication (NFC) and hardware security elements will be investigated for both wired and wireless connectivity. The talk will give an overview of the achieved project results so far and their potential impact on robotics and other applications.

The development of novel technologies for smart textiles is the focus of this research. Improving the level of integration is enabled by a hybrid approach. Elastic and stretchable substrates ensure the integration of electronics into textile products while maintaining the flexible and elastic properties typical of textiles. The sensor system is integrated into a patient monitoring system.

Flexible pressure sensors based on elastomer (generally PDMS) layers are strongly demanded for wearable applications either in everyday life or in biomedical applications. These sensors are designed to be manufactured either in fabrics or in roll-to-roll systems and be disposable sensors, i.e. very low cost sensors. Current trend in research tends to use carbon nanotubes (CNT) in order to improve the sensitivity either of piezoresistive or capacitive force/pressure sensors. The sensitivity can then be improved but in many cases, the effective measurement range is reduced. Furthermore, using and spreading CNT presents a high risk of regulatory issues. An alternative to the use of added materials can be patterning the elastomer layers or to use foams. The sensitivity can be then improved up to 700% compared to bulk ones while keeping the force range in a comparable extent. Furthermore, the experimental data suggest that a physical limit exists and that the range and sensitivity can be tuned while keeping the range sensitivity product at its maximum possible value.

The context of a dazzling development of connected objects worldwide raises the question of their energy autonomy. An attractive approach consists in harvesting energy from ambient heat, light or mechanical vibrations. In the case of ambient vibrations, the considered transduction devices are mostly based on magnetic, piezoelectric and electrostatic technologies. This paper focuses on electrostatic MEMS, which enable extreme miniaturization. The presented Silicon-on-Glass technology brings advantage of very low parasitic capacitance. Up to 1:100 silicon etching aspect-ratio obtained using DRIE enables it to get large capacitance density, leading to high energy density and high electromechanical coupling. In addition, the presented fabrication process requires only two photolithography masks and a very limited number of technological steps, making it affordable for industry and consumer applications. Experimental results show state-of-the art power densities, in the range of 100-300 microwatts per cubic centimeter. Finally, simple and efficient energy extraction interface circuits designed for this family of electrostatic MEMS are presented.

Microelectromechanical systems (MEMS) are based on the deformation, motion or vibrations of mechanical microstructures made in semiconductors, piezoelectric materials and/or metals. Optical techniques are commonly used for more than 20 years in our institute and worldwide for the non contact characterization of the fabrication process and (electro)(thermo)mechanical behavior of these microstructures with a vertical resolution in the nanometer range or below and a lateral resolution in the micrometer range. This paper will report on recent results obtained on various films and MEMS physical microsensors with home-made monochromatic or white light interference microscopy systems and with other optical systems. An emphasis will be put on less common or new measurements allowed by extension of the capabilities of these techniques such as fast full field detection of released parts, 3D profile of etched walls, film (thermo)mechanical stress measurements at the die level, full field in-plane and out-of plane vibration measurements as function of temperature and pressure, and other ones.

The safety of nuclear power plants has always been one of the most important security issues in the industry in general. Numerous standards, techniques, and tools have been developed to deal specifically with the safety of nuclear power plants – one has specialized probes, robotized systems, electronics, and software. Although seen as a mature (or slowly evolving) industry, this notion about nuclear safety is a bit misleading – the area is developing in many promising new directions. Some recent global events will speed up this development even more. On the other hand, the industry is currently going through digital transformation, which brings networking of devices, equipment, computers, and humans. This fourth industrial revolution promises speed, reliability, and efficiencies not possible up until now. In the NDE sector, new production techniques and traditional manufacturing lines are getting to be lights-out operations (near-total automation). The same is most probably going to happen with the safety inspections and quality insurance. Robotics and automation are improving worker safety and reducing human error. The well-being of inspectors working in a hazardous environment is being taken care of. Most experts agree that the digitalization of NDE offers unprecedented opportunities to the world of inspection for infrastructure safety, inspector well-being, and even product design improvements. While the community tends to agree on the value proposition of digital transformation of NDE, it also recognizes the challenges associated with such a major shift in a well-established and regulated sector. The work in this presentation shows a part of the project that aims to develop an original modular ultrasound diagnostic NDE system (consisting of exchangeable transducers, electronics, and acquisition/analysis software algorithms), for applications in hazardous environments within nuclear power plants and similar industrial environments. The presentation will show how the combination robust transducers & electronics & software could, in future, reach near-total automation by implementing resistive piezoelectric materials technology and various deep learning algorithms. Furthermore, future general prospects of this technology are discussed, and how this technology can affect the well-being of nuclear power plant inspectors and contribute to overall plant safety.

The extreme requirements of high-volume audio applications such as smartphones, headphones and voice assistants have led to the development of reliable high-performance MEMS microphone, circuits and integration technologies. In addition to audio applications, this technology enables several new sensing principles, such as photoacoustic spectroscopy and photoacoustic imaging. The high performance of capacitive and piezoresistive MEMS systems pushes the boundaries of ultrasound imaging and sensing technologies. This talk will review of Infineons existing MEMS-based audio and ultrasound systems and the impressive new applications of photoacoustic spectroscopy in gas sensing and living matter photoacoustic sensing and imaging. The high SNR of these MEMS semiconductor systems, combined with RADAR-like signal processing and Ultrasound signals, opens the door for high-resolution ultrasound sensing and imaging applications,  including low-cost point-of-care systems.

Technical solutions for integrating precise mechanics of mechanical wristwatch with ultra-low-power electronics module are presented. Small size, ultra-low power, and accurate control of watch mechanics were the main challenges to overcome. The electronic module gives the watch some smart features through interactions with watch mechanics, Bluetooth low energy connectivity, and accelerometer. The focus will be to present solutions and challenges of precisely measuring the positions of three watch hands which is done using magnetic encoders and RGB sensors as well as challenges we faced with ultra-low power wrist-worn device tap detection.

Wearable, intelligent, and unobtrusive smart sensor nodes that monitor the surrounding environment and even the human body have the potential to create valuable data for a wide range of applications. To attain this vision of unobtrusiveness, smart devices have to gather and analyze data over long periods of time without the need for battery frequent recharging and replacement. On the other hand, advances in low-power electronics and tiny machine learning techniques lead to many novel IoT devices making them more and more intelligent to take autonomous and low latency decisions. On the other hand, these devices have limited computational power due to the constrain of working with minimal energy to maximize the battery lifetime, thus machine learning needs to be adapted to overcome the memory and computational limits.  To, even more, prolong the energy autonomy, energy harvesting from ambient sources is a promising solution to power these low-energy IoT devices.  This talk presents a broad overview of the combination of Tiny Machine learning, low power design, and energy harvesting, when possible, to achieve truly unobtrusive wireless smart IoT devices. As those devices are still strongly application-specific the talk includes some examples of where that combination can be a winning solution facing real application scenarios ranging from smart biomedical patches to autonomous vehicles where the energy harvesting is not possible but the low latency and autonomy brought from the tiny ML is crucial.

Novel dToF applications like augmented / virtual reality and advanced photo enhancement demand for products that simultaneously target a small footprint, low-power operation, as well as a competitive price point. This requires a high level of optimization of the system architecture, paired with a cutting edge technology to meet the application requirements. The presented technology platform is based on a 22nm/45nm 3D-stacked BSI SPAD CMOS process featuring high performance mixed-signal implementations of the fundamental dTOF building blocks. Design considerations, system trade-offs, and circuit realizations are presented. Challenges in system integration are discussed and a detailed insight is given into characterization results.   

Lighting systems are a promising candidate for the Internet of Things backbone, integrating sensors and communication modules. Collecting data from a dense network of smart sensors, together with advanced machine learning techniques, will enable providing information and services beyond lighting, such as about building utilization, or about environmental quality influencing people's comfort and wellbeing. This talk will give an overview of different sensing technologies for enhanced human detection, as well as some related development and pre-development activities within smart lighting systems.