Technical Library | 2020-07-22 19:24:33.0
Recent advancements in electronic packaging and image processing techniques have opened the possibility for optics-based portable eye tracking approaches, but technical and safety hurdles limit safe implementation toward wearable applications. Here, we introduce a fully wearable, wireless soft electronic system that offers a portable, highly sensitive tracking of eye movements (vergence) via the combination of skin-conformal sensors and a virtual reality system. Advancement of material processing and printing technologies based on aerosol jet printing enables reliable manufacturing of skin-like sensors, while the flexible hybrid circuit based on elastomer and chip integration allows comfortable integration with a user's head. Analytical and computational study of a data classification algorithm provides a highly accurate tool for real-time detection and classification of ocular motions. In vivo demonstration with 14 human subjects captures the potential of the wearable electronics as a portable therapy system, whose minimized form factor facilitates seamless interplay with traditional wearable hardware.
Technical Library | 2020-09-08 16:43:32.0
Atomic layer deposition (ALD) is an ultra-thin film deposition technique that has found many applications owing to its distinct abilities. They include uniform deposition of conformal films with controllable thickness, even on complex three-dimensional surfaces, and can improve the efficiency of electronic devices. This technology has attracted significant interest both for fundamental understanding how the new functional materials can be synthesized by ALD and for numerous practical applications, particularly in advanced nanopatterning for microelectronics, energy storage systems, desalinations, catalysis and medical fields. This review introduces the progress made in ALD, both for computational and experimental methodologies, and provides an outlook of this emerging technology in comparison with other film deposition methods. It discusses experimental approaches and factors that affect the deposition and presents simulation methods, such as molecular dynamics and computational fluid dynamics, which help determine and predict effective ways to optimize ALD processes, hence enabling the reduction in cost, energy waste and adverse environmental impacts. Specific examples are chosen to illustrate the progress in ALD processes and applications that showed a considerable impact on other technologies.
Technical Library | 2020-10-08 01:01:01.0
Flexible hybrid electronics (FHE) is emerging as a promising solution to combine the benefits of printed electronics and silicon technology. FHE has many high-impact potential areas, such as wearable applications, health monitoring, and soft robotics, due to its physical advantages, which include light weight, low cost and the ability conform to different shapes. However, physical deformations in the field can lead to significant testing and validation challenges. For example, designers must ensure that FHE devices continue to meet their specs even when the components experience stress due to bending. Hence, physical deformation, which is hard to emulate, has to be part of the test procedures for FHE devices. This paper is the first to analyze stress experience at different parts of FHE devices under different bending conditions. We develop a novel methodology to maximize the test coverage with minimum number of text vectors with the help of a mixed integer linear programming formulation. We validate the proposed approach using an FHE prototype and COMSOL Multiphysics simulations
Technical Library | 2021-11-22 20:39:44.0
Quality control is a key activity performed by manufacturing companies to verify product conformance to the requirements and specifications. Standardized quality control ensures that all the products are evaluated under the same criteria. The decreased cost of sensors and connectivity enabled an increasing digitalization of manufacturing and provided greater data availability. Such data availability has spurred the development of artificial intelligence models, which allow higher degrees of automation and reduced bias when inspecting the products. Furthermore, the increased speed of inspection reduces overall costs and time required for defect inspection. In this research, we compare five streaming machine learning algorithms applied to visual defect inspection with real world data provided by Philips Consumer Lifestyle BV. Furthermore, we compare them in a streaming active learning context, which reduces the data labeling effort in a real-world context. Our results show that active learning reduces the data labeling effort by almost 15% on average for the worst case, while keeping an acceptable classification performance. The use of machine learning models for automated visual inspection are expected to speed up the quality inspection up to 40%.
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