Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks

Ooi, Kai Siang; Kong, Chun Lei; Goay, Chan Hong; Ahmad, Nur Syazreen; Goh, Patrick

doi:10.1007/s00521-019-04252-3

Cited by 9 publications

(12 citation statements)

References 26 publications

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“…The more hidden layers, the more complex the nonlinear relationship between the input and output. Because neural networks have a very strong nonlinear learning ability, they are widely used in the eye diagram prediction of microsystem interconnection structure [ 30 ], crosstalk analysis [ 37 ], frequency domain analysis [ 58 ], parasitic parameter extraction [ 59 ] and other fields. The traditional complex and time-consuming EM or FEM can quickly predict the performance parameters of microsystems [ 60 ].…”

Section: Fast Prediction Of Microsystem Performance By Neural Networkmentioning

confidence: 99%

“…As the frequency increases, the signal crosstalk between different signal transmission lines cannot be neglected. On the other hand, ANN is used to predict the crosstalk at the proximal and distal ends of coupled microstrip transmission lines [ 37 ]. First, a physical model of the microstrip transmission line is constructed, as shown in Figure 3 d. In the microstrip transmission line, six physical parameters and four properties are chosen as the input and output of the ANN, respectively.…”

Section: Fast Prediction Of Microsystem Performance By Neural Networkmentioning

confidence: 99%

“…( d ) Geometrical structure of a coupled stripline. Adapted with permission from [ 37 ]. ( e ) Geometrical structure of a coupled stripline.…”

Section: Figurementioning

confidence: 99%

“…( e ) Geometrical structure of a coupled stripline. Adapted with permission from [ 37 ] ( f ) Testing result of the ANN for crosstalk in striplines. Adapted with permission from [ 37 ].…”

Section: Figurementioning

confidence: 99%

“…In recent years, AI, as a modern discipline, has been widely used in performance prediction [ 37 , 38 , 39 , 40 ], floor planning [ 22 , 41 , 42 ], collaborative optimization [ 43 , 44 , 45 ], image recognition [ 46 , 47 , 48 , 49 ], defect detection [ 50 , 51 , 52 , 53 ], micromanufacturing processes [ 54 , 55 ] and other aspects of research, and has been successfully applied in microsystem SI design. The application of artificial intelligence methods to microsystem design is commonly divided into four steps [ 56 ]: (1) clarify the problem to be solved, determine the design parameters and performance parameters; (2) obtain data; (3) establish a neural networks model and use the acquired data to train neural networks to achieve performance prediction; and (4) optimize the performance through an intelligent optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

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

Shan

Wang

et al. 2023

Micromachines

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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.

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Section: Fast Prediction Of Microsystem Performance By Neural Networkmentioning

confidence: 99%

Section: Fast Prediction Of Microsystem Performance By Neural Networkmentioning

confidence: 99%

“…( d ) Geometrical structure of a coupled stripline. Adapted with permission from [ 37 ]. ( e ) Geometrical structure of a coupled stripline.…”

Section: Figurementioning

confidence: 99%

“…( e ) Geometrical structure of a coupled stripline. Adapted with permission from [ 37 ] ( f ) Testing result of the ANN for crosstalk in striplines. Adapted with permission from [ 37 ].…”

Section: Figurementioning

confidence: 99%