Parasitic capacitance modeling and measurements of conductive yarns for e-textile devices

Qu, Z.; Zhu, Z.; Liu, Yulong; Yu, Mengmeng; Ye, Terry Tao

doi:10.1038/s41467-023-38319-6

Cited by 2 publications

(2 citation statements)

References 30 publications

(18 reference statements)

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“…The grid design affects the wiring's parasitic capacitance and its projection area on the grid reference plane, where the latter also affects the former. In high-frequency circuits, it is essential to maintain the parasitic capacitance values as low as possible [38], as even small variations in this parameter can cause a significant frequency shift [39].…”

Section: Introductionmentioning

confidence: 99%

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Mei,

Yuan,

Guo

et al. 2024

Sensors

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InGaAs detection systems have been increasingly used in the aerospace field, and due to the high signal-to-noise ratio requirements of short-wave infrared quantitative payloads, there is an urgent need for methods for the rapid and precise evaluation and the optimal design of these systems. The rigid-flex printed circuit board (PCB) is a vital component of InGaAs detectors, as its grid ground plane design parameters impact parasitic capacitance and thus affect weak infrared analog signals. To address the time-intensive and costly nature of design optimization achieved with simulations and experimental measurements, we propose an innovative method based on a neural network to predict the scattering parameters of rigid-flex boards for InGaAs detection links. This is the first study in which the effects of rigid-flex boards on weak infrared detection signals have been considered. We first obtained sufficient samples with software simulation. A backpropagation (BP) neural network prediction model was trained on existing sample sets and then verified on a rigid-flex board used in a crucial aerospace short-wave infrared quantitative mission. The model efficiently and accurately predicted high-speed interconnect scattering parameters under various rigid-flex board grid plane parameter conditions. The prediction error was less than 1% compared with a 3D field solver, indicating an overcoming of the iterative optimization inefficiency and showing improved design quality for InGaAs detection circuits.

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Section: Introductionmentioning

confidence: 99%

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Mei,

Yuan,

Guo

et al. 2024

Sensors

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show abstract

“…[ 19–22 ] In recent years, intensive efforts have been devoted to develop textile integrated tactile sensors such as piezoresistive, capacitive, and piezoelectric sensors in human grasp detection and physiological signal monitoring. [ 23–28 ] However, piezoresistive and capacitive tactile sensors require an external power supply to operate, which increases the complexity of the measuring system. [ 29,30 ] Piezoelectric sensors belong to self‐powered tactile sensors but suffer from complicated fabrication processes and high costs.…”

Section: Introductionmentioning

confidence: 99%

Electrostatic Smart Textiles for Braille‐To‐Speech Translation

Li,

Liu,

et al. 2024

Advanced Materials

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Wearable Braille‐to‐speech translation system is of great importance for providing auditory feedback in assisting blind people and people with speech impairment. However, previous reported Braille‐to‐speech translation systems still need to be improved in terms of comfortability or integration. Here, we report a Braille‐to‐speech translation system that uses dual‐functional electrostatic transducers which are made of fabric‐based materials and can be integrated into textiles. Based on electrostatic induction, the electrostatic transducer can either serve as a tactile sensor or a loudspeaker with the same design. The proposed electrostatic transducers have excellent output performances, mechanical robustness, and working stability. By combining the devices with machine learning algorithms, it is possible to translate Braille alphabet and 40 commonly used words (extensible) into speech with an accuracy of 99.09% and 97.08%, respectively. This work demonstrates a new approach for further developments of advanced assistive technology towards improving the life of disabled people.This article is protected by copyright. All rights reserved

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Parasitic capacitance modeling and measurements of conductive yarns for e-textile devices

Cited by 2 publications

References 30 publications

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN

Electrostatic Smart Textiles for Braille‐To‐Speech Translation

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