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The signal integrity (SI) in microsystems has a significant impact on their performance. It is critical to build accurate and high-speed SI prediction models in microsystems and intelligently optimize them. Recently, neural networks (NN) and heuristic optimization algorithms have been widely applied to predict and optimize the SI performance of microsystems. In this work, the SI prediction methods using neural networks, including Artificial Neural Network (ANN), Deep Neural Network (DNN), Recurrent Neural Network (RNN), Convolution Neural Network (CNN), and neural networks using prior knowledge, are systematically summarized. Intelligent optimization algorithms for SI, such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Deep Partition Tree Bayesian Optimization (DPT-BO), and Two-Stage Bayesian Optimization (TSBO), are considered. The comparative analysis of the characteristics and application areas of modern methods has been carried out and their development prospects have been predicted.

Key words: microsystems, signal integrity, neural networks, optimization algorithms
Published in: INFORMATION-COMMUNICATION TECHNOLOGIES
Bibliography link: Shan Guangbao, Li Guoliang, Wang Yuxuan, Qi Peihan, Goncharenko V. I., Volkov A. S., Muratchaev S. S. Application and prospects of artificial intelligence methods in predicting and optimizing signal integrity in microsystems. Izv. vuzov. Elektronika = Proc. Univ. Electronics. 2025;30(6):795–805. (In Russ.). https://doi.org/10.24151/1561-5405-2025-30-6-795-805.

Shan Guangbao
Xidian University of Electronic Science and Technology, Xi’an, China, 710126, Xi’an, Shaanxi, 266 Xinglong Section of Xifeng Road)

Li Guoliang
Xidian University of Electronic Science and Technology, Xi’an, China, 710126, Xi’an, Shaanxi, 266 Xinglong Section of Xifeng Road)

Wang Yuxuan
Xidian University of Electronic Science and Technology, Xi’an, China, 710126, Xi’an, Shaanxi, 266 Xinglong Section of Xifeng Road)

Qi Peihan
Xidian University of Electronic Science and Technology, Xi’an, China, 710126, Xi’an, Shaanxi, 266 Xinglong Section of Xifeng Road)

Vladimir I. Goncharenko
Moscow Aviation Institute (National Research University), Russia, 125993, Moscow, Volokolamskoe hwy, 4

Aleksey S. Volkov
National Research University of Electronic Technology, Russia, 124498, Moscow, Zelenograd, Shokin sq., 1

Sultansaid S. Muratchaev
National Research University of Electronic Technology, Russia, 124498, Moscow, Zelenograd, Shokin sq., 1

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Information about the publication

UDK: 004.89.032.26:621.3.049.77
Publication type: Scientific article
Source lang: Russian
DOI: 10.24151/1561-5405-2025-30-6-795-805
RISC id: KBMPFC

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