Technical Library: onboard (Page 1 of 1)

Corrosion Analysis

Technical Library | 2019-06-03 15:32:40.0

ACI Technologies was pleased to assist a customer by conducting elemental analysis on several assemblies displaying severe corrosion. Several board assemblies had failed in the field and exhibited areas of corrosion in close proximity to onboard components. The most common source of corrosion on electronic assemblies is residual flux. Fluxes are specific chemistries applied during the soldering process which improve the wetting of the solder to both the pad and component when forming the solder joint. They can be highly reactive chemicals that, if left on the assemblies, can lead to corrosion, electrical degradation, and decreased reliability. In the presence of moisture and electrical bias, flux residue can enable dendritic growth as a result of electrochemical migration (ECM).

ACI Technologies, Inc.

All-in-One, Wireless, Stretchable Hybrid Electronics for Smart, Connected, and Ambulatory Physiological Monitoring

Technical Library | 2020-08-19 19:13:00.0

Commercially available health monitors rely on rigid electronic housing coupled with aggressive adhesives and conductive gels, causing discomfort and inducing skin damage. Also, research-level skin-wearable devices, while excelling in some aspects, fall short as concept-only presentations due to the fundamental challenges of active wireless communication and integration as a single device platform. Here, an all-in-one, wireless, stretchable hybrid electronics with key capabilities for real-time physiological monitoring, automatic detection of signal abnormality via deep-learning, and a long-range wireless connectivity (up to 15 m) is introduced. The strategic integration of thin-film electronic layers with hyperelastic elastomers allows the overall device to adhere and deform naturally with the human body while maintaining the functionalities of the on-board electronics. The stretchable electrodes with optimized structures for intimate skin contact are capable of generating clinical-grade electrocardiograms and accurate analysis of heart and respiratory rates while the motion sensor assesses physical activities. Implementation of convolutional neural networks for real-time physiological classifications demonstrates the feasibility of multifaceted analysis with a high clinical relevance. Finally, in vivo demonstrations with animals and human subjects in various scenarios reveal the versatility of the device as both a health monitor and a viable research tool.

Georgia Institute of Technology

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