Electromagnetic Pattern Extraction and Grouping for Near-Field Scanning of Integrated Circuits by PCA and <i>K</i>-Means Approaches

Hong, Ziyang; Fang, Wenxiao; En, Yunfei; Luo, Chengyang; Shao, Weiheng; Wang, Lei; He, Zhiyuan; Shao, E

doi:10.1109/temc.2018.2890026

Cited by 10 publications

(3 citation statements)

References 24 publications

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“…In (12) the selection criterion for a frequency range is the result of several computations (NFreq times) of the selection criterion at each frequency value of this range. Consequently, the selection criterion in a large frequency range is not impacted by the noise level variation over the same frequency band.…”

Section: A A-approach -Approach Based On the Full Frequency Range Mea...mentioning

confidence: 99%

“…power converter, low frequency microcontroller, high frequency FPGA). The frequency range segmentation following the different activities running on the DUT, as proposed by [12], could be an interesting approach.…”

Section: B B-approach -Approach Based On the Spectrum Of Emission Of Dutmentioning

confidence: 99%

“…An analysis of the initial 𝐹 𝑁 dataset (e.g. from [12]) should define the optimal number of the frequency sub-ranges according to the frequency distribution of the sources on the DUT. This one would require a complex algorithm that hamper the deployment of this approach.…”

Section: B B-approach -Approach Based On the Spectrum Of Emission Of Dutmentioning

confidence: 99%

See 2 more Smart Citations

Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multifrequency Near-Field Scanning Measurement

Serpaud¹,

Boyer

Dhia

et al. 2022

IEEE Trans. Electromagn. Compat.

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Near-field scanning is an attractive method to diagnose conducted and radiated emission issues at board or circuit level. However, the long measurement time to scan a dense electronic board at numerous frequencies is a major drawback that limits its growth in industry. To overcome this problem, methods to accelerate the measurement, such as the sequential spatial adaptive sampling algorithm, must be found. This method was developed to speed up a near-field scans measured in frequency domain at only one frequency. The proposed paper capitalizes on this method to extend its principle and demonstrate its effectiveness for near-field scans measured at multiple frequencies. Several measurement strategies and frequency segmentation approaches are compared. A definition of the best strategy and approach to allow a time reduction factor of almost one order magnitude compared to the full sampling measurement has been proposed. Keywords -Electromagnetic compatibility (EMC), near-field scanning (NFS), adaptive sampling, time optimization.* The speed motion of the probe was fixed at Speed = 20 cm/s (probe acceleration and deceleration phases are not taken into account). ** The time to capture all frequency range for one point is fixed at 2.25s.

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Section: A A-approach -Approach Based On the Full Frequency Range Mea...mentioning

confidence: 99%

Section: B B-approach -Approach Based On the Spectrum Of Emission Of Dutmentioning

confidence: 99%

See 1 more Smart Citation

Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multifrequency Near-Field Scanning Measurement

Serpaud¹,

Boyer

Dhia

et al. 2022

IEEE Trans. Electromagn. Compat.

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A New Neural Network Based on CNN for EMIS Identification

et al. 2022

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Pattern clustering method of magnetic near‐field radiation emissions based on DBSCAN algorithm

Hou,

Ding,

Shao

et al. 2024

IET Science Measure & Tech

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The analysis of magnetic near‐field radiation emissions (MNRE) has recently raised more attention in device‐level electromagnetic compatibility testing. Pattern clustering of MNRE for integrated circuits manually is very time‐consuming because of the multi‐dimensional characteristics of MNRE, such as frequency, spatial position, emission intensity, etc. This paper proposes a novel pattern clustering method of MNRE, including strong emission frequency extraction, feature extraction, and density‐based clustering. Ring oscillator and five working states are designed on a Field Programmable Gate Array with 256 Ball Grid Array package, which are used to create a complex multi‐source emission case for verifying the effectiveness of the clustering method. The verification results show that the proposed method can correctly cluster the multi‐source emission patterns. Further, the method is also applied to a Microcontroller Unit with unknown operating states; the results show that the proposed method also can effectively distinguish the unknown emission patterns and locate the unknown interference source. The accuracy of the interference source location is proven by 3D X‐ray microscope inspection.

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Electromagnetic Pattern Extraction and Grouping for Near-Field Scanning of Integrated Circuits by PCA and K-Means Approaches

Cited by 10 publications

References 24 publications

Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multifrequency Near-Field Scanning Measurement

Efficiency of Sequential Spatial Adaptive Sampling Algorithm to Accelerate Multifrequency Near-Field Scanning Measurement

A New Neural Network Based on CNN for EMIS Identification

Pattern clustering method of magnetic near‐field radiation emissions based on DBSCAN algorithm

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