Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN
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 rigi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/7/2221 |
| _version_ | 1797211942663749632 |
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| author | Jingling Mei Haiyue Yuan Xinxin Guo Xiuqin Chu Lei Ding |
| author_facet | Jingling Mei Haiyue Yuan Xinxin Guo Xiuqin Chu Lei Ding |
| author_sort | Jingling Mei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:34:30Z |
| format | Article |
| id | doaj.art-5528c093d3bd442c8fc2b22887609266 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:30Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5528c093d3bd442c8fc2b228876092662024-04-12T13:26:31ZengMDPI AGSensors1424-82202024-03-01247222110.3390/s24072221Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANNJingling Mei0Haiyue Yuan1Xinxin Guo2Xiuqin Chu3Lei Ding4Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaKey Laboratory of Ultra High Speed Circuit Design and Electromagnetic Compatibility, Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Ultra High Speed Circuit Design and Electromagnetic Compatibility, Ministry of Education, Xidian University, Xi’an 710071, ChinaKey Laboratory of Ultra High Speed Circuit Design and Electromagnetic Compatibility, Ministry of Education, Xidian University, Xi’an 710071, ChinaShanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, ChinaInGaAs 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.https://www.mdpi.com/1424-8220/24/7/2221scattering parametersInGaAs detection systemsBP neural networkrigid-flex boardsgrid ground plane |
| spellingShingle | Jingling Mei Haiyue Yuan Xinxin Guo Xiuqin Chu Lei Ding Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN Sensors scattering parameters InGaAs detection systems BP neural network rigid-flex boards grid ground plane |
| title | Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN |
| title_full | Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN |
| title_fullStr | Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN |
| title_full_unstemmed | Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN |
| title_short | Fast Prediction Method for Scattering Parameters of Rigid-Flex PCBs Based on ANN |
| title_sort | fast prediction method for scattering parameters of rigid flex pcbs based on ann |
| topic | scattering parameters InGaAs detection systems BP neural network rigid-flex boards grid ground plane |
| url | https://www.mdpi.com/1424-8220/24/7/2221 |
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