Improving Signal Integrity by Reducing Mode Conversion in Differential Pairs on a Meshed Ground in Flexible Printed Circuit Boards

Yu, Seulgi; Kam, Dong-Gun

doi:10.26866/jees.2022.3.r.81

Cited by 1 publication

(1 citation statement)

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“…Grid ground planes are quite commonly used in a modern rigid-flex PCB design to meet the requirements of impedance matching and interlayer interconnections [6][7][8][9][10]. Traces over the grid ground plane are associated with signal integrity (SI) degradation, electromagnetic interference (EMI), and ground bounce in general [11].…”

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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