Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions

Hakki Mert Torun; Colin Pardue; Mohamed L.F. Belleradj; Anto K. Davis; Madhavan Swaminathan

doi:10.1109/ECTC.2018.00358

Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions

Hakki Mert Torun, Colin Pardue, Mohamed L.F. Belleradj, Anto K. Davis, Madhavan Swaminathan

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Scopus citations

Abstract

Increasing application scope of Internet of Things (IoT) devices have resulted in strict design requirements such as compact systems and efficient power delivery architectures to reduce battery wastage. RF Wireless Power Transfer (WPT) have shown to be promising to address these issues, but with the cost of increased design complexity. In this work, we use machine learning based optimization to select an optimal set of control parameters of a WPT architecture and miniaturize the system while increasing the overall RF-DC conversion efficiency.

Original language	English (US)
Title of host publication	Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2374-2381
Number of pages	8
ISBN (Print)	9781538649985
DOIs	https://doi.org/10.1109/ECTC.2018.00358
State	Published - Aug 7 2018
Event	68th IEEE Electronic Components and Technology Conference, ECTC 2018 - San Diego, United States Duration: May 29 2018 → Jun 1 2018

Publication series

Name	Proceedings - Electronic Components and Technology Conference
Volume	2018-May
ISSN (Print)	0569-5503

Conference

Conference	68th IEEE Electronic Components and Technology Conference, ECTC 2018
Country/Territory	United States
City	San Diego
Period	5/29/18 → 6/1/18

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.1109/ECTC.2018.00358

Cite this

Torun, H. M., Pardue, C., Belleradj, M. L. F., Davis, A. K., & Swaminathan, M. (2018). Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions. In Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018 (pp. 2374-2381). Article 8429871 (Proceedings - Electronic Components and Technology Conference; Vol. 2018-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ECTC.2018.00358

Torun, Hakki Mert ; Pardue, Colin ; Belleradj, Mohamed L.F. et al. / Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions. Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2374-2381 (Proceedings - Electronic Components and Technology Conference).

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abstract = "Increasing application scope of Internet of Things (IoT) devices have resulted in strict design requirements such as compact systems and efficient power delivery architectures to reduce battery wastage. RF Wireless Power Transfer (WPT) have shown to be promising to address these issues, but with the cost of increased design complexity. In this work, we use machine learning based optimization to select an optimal set of control parameters of a WPT architecture and miniaturize the system while increasing the overall RF-DC conversion efficiency.",

author = "Torun, {Hakki Mert} and Colin Pardue and Belleradj, {Mohamed L.F.} and Davis, {Anto K.} and Madhavan Swaminathan",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 68th IEEE Electronic Components and Technology Conference, ECTC 2018 ; Conference date: 29-05-2018 Through 01-06-2018",

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doi = "10.1109/ECTC.2018.00358",

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Torun, HM, Pardue, C, Belleradj, MLF, Davis, AK & Swaminathan, M 2018, Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions. in Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018., 8429871, Proceedings - Electronic Components and Technology Conference, vol. 2018-May, Institute of Electrical and Electronics Engineers Inc., pp. 2374-2381, 68th IEEE Electronic Components and Technology Conference, ECTC 2018, San Diego, United States, 5/29/18. https://doi.org/10.1109/ECTC.2018.00358

Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions. / Torun, Hakki Mert; Pardue, Colin; Belleradj, Mohamed L.F. et al.
Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2374-2381 8429871 (Proceedings - Electronic Components and Technology Conference; Vol. 2018-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Swaminathan, Madhavan

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AB - Increasing application scope of Internet of Things (IoT) devices have resulted in strict design requirements such as compact systems and efficient power delivery architectures to reduce battery wastage. RF Wireless Power Transfer (WPT) have shown to be promising to address these issues, but with the cost of increased design complexity. In this work, we use machine learning based optimization to select an optimal set of control parameters of a WPT architecture and miniaturize the system while increasing the overall RF-DC conversion efficiency.

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Torun HM, Pardue C, Belleradj MLF, Davis AK, Swaminathan M. Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions. In Proceedings - IEEE 68th Electronic Components and Technology Conference, ECTC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2374-2381. 8429871. (Proceedings - Electronic Components and Technology Conference). doi: 10.1109/ECTC.2018.00358

Machine learning driven advanced packaging and miniaturization of IoT for wireless power transfer solutions

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