Optimal frequency for powering millimeter-sized biomedical implants inside an inductively-powered homecage

Hesam Sadeghi Gougheri, Mehdi Kiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

This paper presents the optimal design and operation frequency (f) of an inductively-powered homecage for powering biomedical devices with millimeter (mm) dimensions, implanted inside the body of freely-behaving small animal subjects, for longitudinal behavioral neuroscience and electrophysiology experiments. In order to improve the power transmission efficiency (PTE) for powering mm-sized implants, the geometry of the multi-coil inductive links in the form of 3- and 4-coil links as well as fp need to be co-optimized. A simplified equation for the PTE of 3-coil inductive links for powering mm-sized implants has been derived, based on which the optimal geometries and fp of a 3-coil link have been found using a commercial field solver (HFSS). In simulations, the optimized 3-coil inductive link achieved a significant PTE of 2.56% at the optimal fp of 40 MHz for powering a 1 mm3 implant coil at the nominal height of 7 cm, thanks to the link and fp optimization as well as an intermediate coil in the receiver side with 18 mm diameter.

Original languageEnglish (US)
Title of host publication2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4804-4807
Number of pages4
Volume2016-October
ISBN (Electronic)9781457702204
DOIs
StatePublished - Oct 13 2016
Event38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 - Orlando, United States
Duration: Aug 16 2016Aug 20 2016

Other

Other38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016
Country/TerritoryUnited States
CityOrlando
Period8/16/168/20/16

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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