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Reference:
- NATURE ELECTRONICS, June 13, 2022, ¡°Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence,¡± by Chanyeol Choi, Hyunseok Kim, et al. © 2022 Springer Nature Limited. All rights reserved.

To view or purchase this article, please visit:
https://www.nature.com/articles/s41928022-00778-y
[GT] Optimization of a Vibrating MEMS Electromagnetic Energy Harvester Using Simulations

The Internet of Things involves the wireless interconnection of an enormous and rapidly expanding number of devices that are generally small and portable. And each of these needs its own sustainable micro-energy source.

Batteries are unsatisfactory for this as they will often need to be replaced or recharged. Many different technologies are being considered and one of the most promising solutions seems to be electromagnetic energy harvesting.

An electromagnetic energy harvester consists of a vibrating plate holding an array of micromagnets facing and coupled with a parallel, static coil. Electrical energy is generated by the vibrating magnets and the amount of electricity that can enter a circuit depends on the design of the coil, the magnet and the spacing between them.

As documented recently in The European Physical Journal Special Topics, a team of French and American researchers investigated a system using magnets which combined the rare earth metal neodymium with iron and boron.

They found that power could be optimized through a trade-off between the spacing of the magnets in the array and the number of turns in the coil; reducing the distance between coil and magnet array while increasing the thickness of the magnets also increased the power.

The researchers are now producing harvesters using the guidelines they developed through this study. And these devices are likely to prove useful for the Internet of Things in aerospace, automotive, biomedical and other sectors.

NATURE ELECTRONICS, June 13, 2022, ¡°Reconfigurable heterogeneous integration using stackable chips with embedded artificial intelligence,¡± by Chanyeol Choi, Hyunseok Kim, et al. © 2022 Springer Nature Limited. All rights reserved.

To view or purchase this article, please visit:
https://www.nature.com/articles/s41928022-00778-y