Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of...

Area of Science:

• Electrical Engineering
• Computer Science
• Materials Science

Background:

• Microsystems are integral to modern technologies like 5G and the Internet of Things (IoT).
• Signal integrity (SI) is a critical performance determinant in these microsystems.
• Accurate and efficient SI modeling is essential for microsystem development.

Purpose of the Study:

• To systematically review neural network (NN) methods for microsystem SI performance prediction.
• To summarize intelligent optimization algorithms used for microsystem SI.
• To compare and discuss the characteristics and applications of current SI modeling techniques.

Main Methods:

• Review of artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) for SI prediction.
• Overview of heuristic optimization algorithms including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), and two-stage Bayesian optimization (TSBO) for SI optimization.
• Comparative analysis of the discussed methods' features and application domains.

Main Results:

• Neural networks offer powerful predictive capabilities for microsystem SI.
• Heuristic algorithms provide effective means for optimizing SI parameters.
• A wide range of NN and optimization techniques are available, each with specific strengths.

Conclusions:

• The study provides a comprehensive overview of NN and heuristic algorithm applications in microsystem SI.
• Understanding these methods is key to advancing SI performance in emerging electronic devices.
• Future research directions in intelligent SI modeling are highlighted.