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Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems
Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently,...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958958/ https://www.ncbi.nlm.nih.gov/pubmed/36838043 http://dx.doi.org/10.3390/mi14020344 |
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author | Shan, Guangbao Li, Guoliang Wang, Yuxuan Xing, Chaoyang Zheng, Yanwen Yang, Yintang |
author_facet | Shan, Guangbao Li, Guoliang Wang, Yuxuan Xing, Chaoyang Zheng, Yanwen Yang, Yintang |
author_sort | Shan, Guangbao |
collection | PubMed |
description | Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized. |
format | Online Article Text |
id | pubmed-9958958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99589582023-02-26 Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems Shan, Guangbao Li, Guoliang Wang, Yuxuan Xing, Chaoyang Zheng, Yanwen Yang, Yintang Micromachines (Basel) Review Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized. MDPI 2023-01-29 /pmc/articles/PMC9958958/ /pubmed/36838043 http://dx.doi.org/10.3390/mi14020344 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Shan, Guangbao Li, Guoliang Wang, Yuxuan Xing, Chaoyang Zheng, Yanwen Yang, Yintang Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title | Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title_full | Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title_fullStr | Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title_full_unstemmed | Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title_short | Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems |
title_sort | application and prospect of artificial intelligence methods in signal integrity prediction and optimization of microsystems |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9958958/ https://www.ncbi.nlm.nih.gov/pubmed/36838043 http://dx.doi.org/10.3390/mi14020344 |
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