Hierarchical Attention-Based Machine Learning Model for Radiation Prediction of WB-BGA Package

Jin, Hailin; Gu, Zheming; Tao, Tuomin; Li, Er‐Ping

doi:10.1109/temc.2021.3075020

Cited by 19 publications

(6 citation statements)

References 22 publications

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“…Jin et al [ 68 ] predicted electromagnetic interference in wire-bonded ball grid array (WB-BGA) packages using an attention module-based DNN model, and the WB-BGA package model and the proposed model are shown in Figure 4 g,f, respectively. The input weights of the DNN are re-derived from a three-layer attention-based module.…”

Section: Fast Prediction Of Microsystem Performance By Neural Networkmentioning

confidence: 99%

“…( f ) Hierarchical attention-based DNN. Adapted with permission from [ 68 ]. ( g ) WB-BGA package model.…”

Section: Figurementioning

confidence: 99%

“…( g ) WB-BGA package model. Adapted with permission from [ 68 ]. ( h ) Comparison of EMI radiation results predicted by DNN model and results measured by far-field experiment.…”

Section: Figurementioning

confidence: 99%

“…( h ) Comparison of EMI radiation results predicted by DNN model and results measured by far-field experiment. Adapted with permission from [ 68 ].…”

Section: Figurementioning

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%

“…( f ) Hierarchical attention-based DNN. Adapted with permission from [ 68 ]. ( g ) WB-BGA package model.…”

Section: Figurementioning

confidence: 99%

“…( g ) WB-BGA package model. Adapted with permission from [ 68 ]. ( h ) Comparison of EMI radiation results predicted by DNN model and results measured by far-field experiment.…”

Section: Figurementioning

confidence: 99%

“…( h ) Comparison of EMI radiation results predicted by DNN model and results measured by far-field experiment. Adapted with permission from [ 68 ].…”

Section: Figurementioning

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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“…Te researchers presented a deep learning approach based on a convolutional neural network combined with far-feld wave data generated from a nearfeld resonant metal body at microwave frequencies for subwavelength imaging in the far-feld [23]. In other studies, researchers used near-feld scanning microscopy or an equivalent set of elemental dipoles methods associated with genetic algorithms [24], convolutional neural networks [25,26], hierarchical attention-based deep neural networks [27], extreme gradient boosting method [28], or strategies based on artifcial neural networks and optimizer algorithm [29,30]. We use the transformer model to capture the relationship on feature maps to establish the correlations between two multivariate data series.…”

Section: Related Workmentioning

confidence: 99%

Field Deviation in Radiated Emission Measurement in Anechoic Chamber for Frequencies up to 60 GHz and Deep Learning-Based Correction

Shi

Yan

Yang

et al. 2022

International Journal of Antennas and Propagation

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The measurement of radiated emission (RE) in an anechoic chamber becomes very challenging at high frequencies, up to 60 GHz, because the scanning plane of the receiver is in measurement standard deviation from the actual wavefront. As a result, the RE intensity of the devices may be underestimated, resulting in electromagnetic interference. The deviation between the electric field at the far-field vertical scanning point and the actual wavefront is researched. Then, in an anechoic chamber, a hybrid deep learning amendment model of convolutional neural network (CNN) and transformer is proposed to correct the RE measurement at a 3 m distance. The results indicate that the correction is reliable, with an average error of 6.35% for a 3 m distance in a semianechoic chamber and less than 4.83% for other test scenarios. The proposed method provides a promising solution for RE measurement at a millimeter wave band in an anechoic chamber.

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A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Xing,

Wang,

Zhou

et al. 2023

Ocean Engineering

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Hierarchical Attention-Based Machine Learning Model for Radiation Prediction of WB-BGA Package

Cited by 19 publications

References 22 publications

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

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

Field Deviation in Radiated Emission Measurement in Anechoic Chamber for Frequencies up to 60 GHz and Deep Learning-Based Correction

A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

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