Improved Probing Reliability in Antenna-on-Chip Measurements

Liu, Qiang; Reniers, A.C.F.; Johannsen, Ulf; Beurden, M.C. van; Smolders, A.B.

doi:10.1109/lawp.2018.2865605

Cited by 11 publications

(4 citation statements)

References 19 publications

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“…In FIGURE 13, the measured E-plane pattern shows a side lobe in the direction opposite to the GSG feeding probe. As documented in the literature [20], [21], this effect is due to the probe itself, which distorts the radiation pattern. with respect to both conventional monolithically integrated monopoles and to more complex architectures employing artificial magnetic conductors (AMC) or partial radiating surfaces (PRC).…”

Section: Resultsmentioning

confidence: 89%

A Gain Levelling Technique for On-Chip Antennas Based on Split-Ring Resonators

et al. 2021

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This paper introduces a gain enhancement technique for monolithically integrated antennas. Such devices can suffer from gain dips within their operating bandwidth due to standing waves arising in the die or caused by interactions with other on-chip components. In this work, it is shown how these effects can be significantly mitigated by parasitically coupling square Split Ring Resonators (SRR) to the fed antenna. The SRRs geometry and their coupling with the master antenna can be set in such a way that they create an additional resonance that cancels gain drops and improves impedance matching. The proposed configuration has been validated using a W-band monopole antenna in a standard 0.13 µm SiGe BiCMOS process. Thanks to the proposed approach, it was possible to compensate a gain drop of about 7 dB at about 85 GHz. As a result, the experimental assessment showed a maximum measured antenna gain of 1.61 dB at 81.5 GHz and an operating bandwidth from 77 to 87 GHz.INDEX TERMS antenna on-chip (AoC), monopole antenna, SiGe BiCMOS, split ring resonator (SRR), MIM capacitors.

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Section: Resultsmentioning

confidence: 89%

A Gain Levelling Technique for On-Chip Antennas Based on Split-Ring Resonators

et al. 2021

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“…displaced a summary of current machine learning solution proposed for smart antenna. [33] NN Forecast the opening antenna form Liuet al [34] ANN Forecast the opening antenna form…”

Section: Refers Year Descriptionmentioning

confidence: 99%

A Review on Machine Learning in Smart Antenna: Methods and Techniques

Sadiq¹,

Sulaiman²,

isa³

et al. 2022

TEM Journal

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According to several research circles, it is predicted that future wireless systems that employ smart antenna techniques would be more effective at using available spectrum and at building new networks at lower cost, while also improving service quality and allowing for cross-technology operation. These systems require constant monitoring in order to function properly, allowing users to apply machine learning algorithms to analyse large amounts of data from various antenna settings. Machine learning is a technique in which a machine learns and improves on its own, based on past data. These techniques enable the smart antenna target to be learned in an efficient, reliable, and adaptive manner. In this paper, the antenna array and antenna developed for the Internet of Things applications were highlighted. In this paper, we review how machine learning techniques can handle these applications effectively and what the concept of adaptive antenna is and when it can be used in this day and age characterized by the rapid development of information technology.

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“…it can be analyzed using a back-to-back probe test [66]. Systematic errors of the cable and the probe itself can be de-embedded using a planar calibration substrate [67]. However, the calibration procedure is not so straightforward.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%