Design of a Single Chip HF-UHF Dual-Band RFID Tag Antenna

Zhao, Yao; Zhang, Yuan; Zhu, Lei; Shen, Lin; Du, Yanping; Yu, Chaohui

doi:10.1007/978-981-19-1673-1_45

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…As a result, the HF antenna is relatively computationally small and the simulation time is comparatively short. As explained in [21], the HF RFID tag antenna has a significant impact on the impedance of the UHF RFID tag antenna, while the latter has a relatively small influence on the former. Therefore, this paper will obtain the structural dimensions of the HF coil antenna with the help of HFSS simulation software and design the UHF antenna based on it.…”

Section: Structure Of the Hf-uhf Rfid Tag Antennamentioning

confidence: 99%

MSCNN-LSTM Model for Predicting Return Loss of the UHF Antenna in HF-UHF RFID Tag Antenna

Zhao¹,

Zhang²,

Li³

et al. 2023

Computers, Materials &Amp; Continua

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High-frequency (HF) and ultrahigh-frequency (UHF) dual-band radio frequency identification (RFID) tags with both near-field and farfield communication can meet different application scenarios. However, it is time-consuming to calculate the return loss of a UHF antenna in a dualband tag antenna using electromagnetic (EM) simulators. To overcome this, the present work proposes a model of a multi-scale convolutional neural network stacked with long and short-term memory (MSCNN-LSTM) for predicting the return loss of UHF antennas instead of EM simulators. In the proposed MSCNN-LSTM, the MSCNN has three branches, which include three convolution layers with different kernel sizes and numbers. Therefore, MSCNN can extract fine-grain localized information of the antenna and overall features. The LSTM can effectively learn the EM characteristics of different structures of the antenna to improve the prediction accuracy of the model. Experimental results show that the mean absolute error (0.0073), mean square error (0.00032), and root mean square error (0.01814) of the MSCNN-LSTM are better than those of other prediction methods. In predicting the return loss of 100 UHF antennas, compared with the simulation time of 4800 s for High Frequency Structure Simulator (HFSS), MSCNN-LSTM takes only 0.927519 s under the premise of ensuring prediction accuracy, significantly reducing the calculation time, which provides a basis for the rapid design of HF-UHF RFID tag antenna. Then MSCNN-LSTM is used to determine the dimensions of the UHF antenna quickly. The return loss of the designed dualband RFID tag antenna is −58.76 and −22.63 dB at 13.56 and 915 MHz, respectively, achieving the desired goal.

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Section: Structure Of the Hf-uhf Rfid Tag Antennamentioning

confidence: 99%