Conference Agenda

Unwanted harmonic signals can be generated by imperfect conductors, contacts, and non-linear magnetic materials. For example, an 860 MHz signal can generate 2580 MHz interference. Harmonic sources can be identified through direct signal injection and a near-field scan at the harmonic frequencies. This non-invasive approach can be used to scan conductive tapes or metal frames of electronic products to find harmonic sources. In this paper, we experiment with single-ended signal injection to excite a passive conductor and scan the harmonic distribution over the surface of the object by using an H-field probe. Through this approach we can initially identify the possible nonlinear sources within a millimeter range of resolution.

The precise identification of electromagnetic interference (EMI) sources is the primary task to be addressed when a product’s radiation emission exceeds compliance standards. However, when multiple sources work at the same frequency but with different time-domain characteristics (e.g., radiation signals are complex modulated signals), it is difficult to directly determine the location of the radiation sources by traditional near-field scanning (NFS) with spectrum analyzer measurements. In this paper, a novel weighted-correlation NFS method is proposed, which can intuitively display the far-field radiation source distribution by calculating the near-to-far field correlation. The proposed method performs the short-term fast Fourier transform (STFFT) analysis on the far-field and near-field signals, then performs deferred cross-correlation analysis on the time-frequency spectrum, and finally calculates the weighted-correlation map based on the NFS results. Two simulation cases are presented to demonstrate the effectiveness of the proposed method in accurately locating far-field radiation sources.

Near-field to far-field transformation is a widely used technique in electromagnetic compatibility testing to predict far-field radiation from near-field measurements. However, at low frequencies, where the device under test is small relative to the wavelength, the transformation often fails, resulting in significant discrepancies between calculated and the actual far-field results. Based on a test case, this paper investigates the underlying causes of this failure by analyzing the effects of cable radiation, limited scan area, probe sensitivity, and other contributing factors. Timedomain near-field measurements of the magnetic and electric fields are conducted using a calibrated scanning system, and the results are compared with full-wave simulations. The findings reveal that improper scan area selection significantly contributes to transformation inaccuracies. Furthermore, the study demonstrates that increasing the scan height can reduce errors caused by the dominance of reactive near-field components which worsens with reduced frequency for a small DUT. A detailed error analysis is provided, offering practical guidelines to improve nearfield to far-field transformations in low-frequency electromagnetic compatibility applications.

The electromagnetic field will spread/shrink between a lower/higher plane and a higher/lower plane. It is difficult to simulate such spreading/shrinking by using traditional methods, such as dipole source reconstruction method. In this paper, a deep convolutional neural network (DCNN) is proposed to “learn” the rule of such spreading/shrinking of electromagnetic fields, and then a novel near-field to near-field transform is proposed. To learn such rule, the proposed DCNN combines a height feature matrix with the scanned near-field pattern. The input of the DCNN is the scanned phaseless near-field at single plane and the height label of the target plane. The output is the field pattern at the target plane. Based on the scanned phaseless near-field at a single plane, the fields at other planes can be quickly derived using the trained DCNN. It significantly enhances the scanning efficiency in electromagnetic interference (EMI) near-field measurement. This method is comprehensively validated through numerical and experimental examples. Moreover, the proposed method shows better precision compared with the traditional dipole source reconstruction method. The corresponding code, data, and well-trained DCNN are all available through https://github.com/DongHaoHan/EMC-EUROPE-Near-Field-Transformation.