Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation

Jinfeng Li

doi:10.1109/IMWS-AMP54652.2022.10107046

Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation

Jinfeng Li^*

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

Machine learning is a versatile but little-understood approach in tunable RF electronics. This paper serves as the first to investigate the feasibility of taking inspiration from machine learning and the recent advances in digital twinning for designing liquid crystals-based reconfigurable devices in the regime of gigahertz (GHz) and terahertz (THz), targeting demanding applications in 5G/6G wireless backhaul links, satellite internet mobile terminals, and smart sensing. States of the arts are reviewed, with a prime design example of liquid crystals-based enclosed coplanar waveguide phase shifter illustrated, highlighting the opportunities offered and challenges encountered.

Original language	English
Title of host publication	IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665478342
DOIs	https://doi.org/10.1109/IMWS-AMP54652.2022.10107046
Publication status	Published - 2022
Externally published	Yes
Event	2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Guangzhou, China Duration: 27 Nov 2022 → 29 Nov 2022

Publication series

Name	IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings

Conference

Conference	2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022
Country/Territory	China
City	Guangzhou
Period	27/11/22 → 29/11/22

Access to Document

10.1109/IMWS-AMP54652.2022.10107046

Cite this

Li, J. (2022). Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation. In IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings (IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IMWS-AMP54652.2022.10107046

Li, Jinfeng. / Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation. IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings).

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title = "Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation",

abstract = "Machine learning is a versatile but little-understood approach in tunable RF electronics. This paper serves as the first to investigate the feasibility of taking inspiration from machine learning and the recent advances in digital twinning for designing liquid crystals-based reconfigurable devices in the regime of gigahertz (GHz) and terahertz (THz), targeting demanding applications in 5G/6G wireless backhaul links, satellite internet mobile terminals, and smart sensing. States of the arts are reviewed, with a prime design example of liquid crystals-based enclosed coplanar waveguide phase shifter illustrated, highlighting the opportunities offered and challenges encountered.",

author = "Jinfeng Li",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 ; Conference date: 27-11-2022 Through 29-11-2022",

year = "2022",

doi = "10.1109/IMWS-AMP54652.2022.10107046",

language = "English",

series = "IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings",

address = "United States",

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Li, J 2022, Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation. in IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings. IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022, Guangzhou, China, 27/11/22. https://doi.org/10.1109/IMWS-AMP54652.2022.10107046

Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation. / Li, Jinfeng.
IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. (IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings).

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

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AB - Machine learning is a versatile but little-understood approach in tunable RF electronics. This paper serves as the first to investigate the feasibility of taking inspiration from machine learning and the recent advances in digital twinning for designing liquid crystals-based reconfigurable devices in the regime of gigahertz (GHz) and terahertz (THz), targeting demanding applications in 5G/6G wireless backhaul links, satellite internet mobile terminals, and smart sensing. States of the arts are reviewed, with a prime design example of liquid crystals-based enclosed coplanar waveguide phase shifter illustrated, highlighting the opportunities offered and challenges encountered.

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M3 - Conference contribution

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Li J. Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation. In IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. (IEEE MTT-S International Microwave Workshop Series on Advanced Materials and Processes for RF and THz Applications, IMWS-AMP 2022 - Proceedings). doi: 10.1109/IMWS-AMP54652.2022.10107046

Machine Learning and Digital Twinning Enabled Liquid Crystals mm-Wave Reconfigurable Devices Design and Systems Operation

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