Shan Guangbao, Li Guoliang, Wang Yuxuan, Xing Chaoyang, Zheng Yanwen, Yang Yintang
School of Microelectronics, Xidian University, Xi'an 710071, China.
Beijing Institute of Aerospace Control Devices, Beijing 100039, China.
Micromachines (Basel). 2023 Jan 29;14(2):344. doi: 10.3390/mi14020344.
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.
微系统广泛应用于5G、物联网、智能电子设备等领域，而信号完整性（SI）决定了它们的性能。为微系统中的SI建立准确、快速的预测模型和智能优化模型极其重要。近年来，神经网络（NNs）和启发式优化算法已被广泛用于预测微系统的SI性能。本文系统地总结了应用于预测微系统SI性能的神经网络方法，包括人工神经网络（ANN）、深度神经网络（DNN）、循环神经网络（RNN）、卷积神经网络（CNN）等，以及应用于微系统SI优化的智能算法，包括遗传算法（GA）、差分进化（DE）、深度划分树贝叶斯优化（DPTBO）、两阶段贝叶斯优化（TSBO）等，并对当前应用方法的特点和应用领域进行了比较和讨论。还预测了未来的发展前景。最后，对本文进行了总结。
