CALCON 2014, KOLKATA, NOV 2014
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Electromagnetic Compatibility Engineering for Electronic Circuits and Devices Peter Russer, Life Fellow, IEEE
Abstract—In this presentation the simulation of near- and farfield propagation of stochastic electromagnetic fields discussed. The modeling of stochastic EM fields is plays a crucial role in the electromagnetic compatibility design of electronic devices, circuits and systems. For the description of stochastic EM fields correlation dyadics are introduced. For the transformation of the field correlation dyadics using Green’s functions are used. The method accounts for arbitrary correlations between the noise radiation sources and allows to compute the spatial distribution of the spectral energy density of noisy electromagnetic sources. The Method of moments (MoM) can be applied to solve noisy electromagnetic field problems by network methods applying correlation matrix techniques. Numerical examples, demonstrating the strong influence of the correlation between the sources on the spatial distribution of the radiated noise field are presented.
Noise in electromagnetic fields plays an important role in wireless communications, electromagnetic sensorics and in radiated electromagnetic interference since it may degrade or obstruct the performance of systems. The origins of electromagnetic noise may be thermal background noise, terrestrial noise and electromagnetic interference radiated by electric and electronic systems. Noise in electromagnetic systems degrades the signal quality in wireless information transmission. Radiated electromagnetic interference can impose severe limitations on the performance of electronic systems. For many design tasks in electronics, especially in the case of computeraided design and manufacturing of electronic systems, accurate modeling of the system performance under the influence of electromagnetic noise and radiated electromagnetic interference is required. The importance of an electromagnetic theory-based in-depth analysis of wireless communication systems, taking impedance matching, antenna mutual coupling, and different sources of noise into consideration, has already been stressed in [1], [2]. Mutual coupling of antenna elements in array antennas introduces limitations on element efficiency and reduces the signal diversity in multi-antenna communication systems [3]. Integrated circuits frequently are the main source of radiated emissions in electronic systems. Near-field characterization of radiating electromagnetic structures allows for a modeling of the electromagnetic field generated by these structures when embedded into a circuit or system environment and therefore can be an efficient tool for computer aided manufacturing of electric and electronic systems. Apart from the cases where radiated electromagnetic noise is only an interference factor for the information carrying electromagnetic field also those cases are of technical imporPeter Russer is with the Institute of Nanoelectronics of the Technische Universit¨at M¨unchen, 80333, Munich, Germany, Email:
[email protected]
tance, where a stochastic or chaotic electromagnetic field is the intrinsic carrier of information or energy. Infrared detectors for the long-wave infrared range (LWIR) are of special interest due to their ability to detect blackbody radiation of objects at room temperature [4], [5]. Such detectors are attractive candidates for various applications including energy harvesting, target tracking, and thermal imaging. Nearfield-coupled nanoantennas with antenna-coupled thermocouples may open to possibility of obtaining carrier phase after envelope demodulation [6]. Due to the equivalence principle [7, p. 106], [8, p. 121] an equivalent source distribution determined by amplitude and phase scanning of the tangential electric or magnetic field on a surface enclosing the radiating structure is equivalent to the internal sources and allows to model the environmental field. Numerous research groups have investigated the characterization of the radiated electromagnetic interference of devices by near-field measurements [9], [10]. The goal has been to establish a model of the device under test that allows modeling the EMI radiated by the device under test into the environment. Due to the lack of numerical tools for the treatment of stochastic electromagnetic fields in literature often methods for deterministic fields are applied to the modeling of stochastic fields. This yields problems if the noise sources are spatially distributed and only partially correlated. Usually we have to consider a partial correlation of the noise sources. In this contribution we present a methodology for the numerical computation of noisy electromagnetic fields excited by spatially distributed noise sources with arbitrary spatial correlation. Throughout this presentation we assume that the considered stochastic signals and stochastic fields are Gaussian random processes [11, p. 154]. This guarantees that auto- and cross correlation functions and spectra, respectively, yield a complete description of the stochastic signals. The scanning of the near-field amplitude distribution only is possible if phase and amplitude of the near field signals are well-defined, i.e. when phase and amplitude signals are fully correlated for any pair of sample points in the near-field. In general this requirement is not fulfilled for the EMI radiated by electronic circuits. Even if the signals flowing in an IC or in a circuit board are deterministic, the complexity of the interdependence of signals flowing in different parts of the circuit areas cannot be revealed by the near-field measurement. Therefore, due to this inherent lack of information the nearfield measurement has to be performed accounting for the statistical nature of the near-field. Due to the lack of information about the sources of the radiated EMI, the near-field to be measured has to be treated as
CALCON 2014, KOLKATA, NOV 2014
a stochastic field. Like in the case of deterministic electromagnetic fields also in the case of stochastic electromagnetic fields network methods can reduce the computational effort considerably and beyond this can contribute to compact wireless link multiport (WLM) model generation. Network methods for deterministic fields have already been described in [8], [12]. Stochastic electromagnetic fields with Gaussian probability distribution can be described completely by the autocorrelation spectrum of each field variable and the cross correlation spectra of field variables at distinct points of observation [13], [14], [15], [16], [17], [18]. Characterization of a stochastic electromagnetic field requires the sampling of the EM field in pairs of observation points and the determination of the cross correlation functions for all pairs of field samples. On the basis of this work network methods for the efficient solution of stochastic electromagnetic field problems were introduced. A method for characterizing and modeling the noisy electromagnetic fields from complex circuit boards using a simplified representation of correlated dipoles has been described in [19]. Depending on the number of statistically independent field sources the eigenvalue decomposition of the correlation matrix yields a compact description of the measured EM noise field [20]. An efficient method for the numerical simulation of nearand far-field propagation of stochastic electromagnetic fields is presented. The method is based on the transformation of field correlation dyadics using Green’s functions or the field transfer functions computed for deterministic fields. We recall the correlation matrix based methods for network oriented noise modeling. We show the simple and general correspondence between the linear network equations describing linear circuits and the corresponding equations governing the relations between the correlation spectra of the noise signals. We introduce the scalar Green’s function method for the computation of a scalar field excited by a scalar stochastic source field. We show that we have to consider the correlations between the exciting field amplitudes at each pair of points. The description of vectorial stochastic fields by correlation dyadics is introduced and the Green’s function method is extended to vectorial stochastic fields. We extend the method of moments (MoM) to treat stochastic electromagnetic fields. The MoM allows to formulate the field problem as an algebraic network problem and to apply the correlation matrix methods from network theory for the treatment of noisy electromagnetic fields. We have applied the MoM for the formulation of the theory since the MoM is a versatile framework for the general formulation and algebraisation of numerical electromagnetic methods as we already have shown for the FDTD and TLM methods [21], [22]. Finally, we present numerical examples of the computation of near- and far-field distributions of electromagnetic fields excited by stochastic sources. and we discuss in a method for full characterization of stochastic fields by tangential two-point correlation near-field scanning in a plane of reference. This presentation is based on [23]. An extended version of this presentation will be published in [24].
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R EFERENCES [1] M. Ivrlac and J. Nossek, “Toward a circuit theory of communication,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 57, no. 7, pp. 1663–1683, Jul. 2010. [2] ——, “The multiport communication theory,” IEEE Circuits and Systems Magazine, vol. 14, no. 3, pp. 27–44, 2014. [3] K. F. Warnick and P. Russer, “Quantifying the noise penalty for a mutually coupled array,” in Antennas and Propagation Society International Symposium, 2008. AP-S 2008. IEEE, 2008, pp. 1–4. [4] P. Russer, J. A. Russer, G. Scarpa, P. Lugli, and W. Porod, “Integrated antennas for rf sensing, wireless communications and energy harvesting,” in Proc. Asia-Pacific Symposium on Electromagnetic Compatibility APEMC, Singapore, 21 - 24 May 2012. [5] P. Russer, J. A. Russer, F. Mukhtar, P. Lugli, S. Wane, D. Bajon, and W. Porod, “Integrated antennas for RF sensing, wireless communications and energy harvesting applications,” in International Workshop on Antenna Technology, iWAT 2013, Karlsruhe, March 4-6 2013. [6] G. P. Szakmany, A. O. Orlov, G. H. Bernstein, W. Porod, M. Bareiss, P. Lugli, J. A. Russer, C. Jirauschek, P. Russer, M. T. Ivrlac, and J. A. Nossek, “Nano-antenna arrays for the infrared regime,” in 18th International ITG Workshop on Smart Antennas (WSA), Erlangen, March 2014. [7] R. F. Harrington, Time Harmonic Electromagnetic Fields. New York: McGraw-Hill, 1961. [8] P. Russer, Electromagnetics, Microwave Circuit and Antenna Design for Communications Engineering, 2nd ed. Boston: Artech House, 2006. [9] J. Shi, M. Cracraft, K. Slattery, M. Yamaguchi, and R. DuBroff, “Calibration and compensation of near-field scan measurements,” IEEE Trans. Electromagn. Compatibility, vol. 47, no. 3, pp. 642–650, 2005. [10] A. Tankielun, U. Keller, E. Sicard, P. Kralicek, and B. Vrignon, “Electromagnetic Near-Field scanning for microelectronic test chip investigation,” IEEE EMC Society Newsletter (October 2006), 2006. [11] W. B. Davenport and W. L. Root, An Introduction to the Theory of Random Signals and Noise. New York: McGraw-Hill, 1958. [12] L. B. Felsen, M. Mongiardo, and P. Russer, Electromagnetic Field Computation by Network Methods. Springer-Verlag, mar 2009. [13] J. Russer and P. Russer, “Stochastic electromagnetic fields,” in German Microwave Conference (GeMIC), 2011. IEEE, Mar. 2011, pp. 1–4. [14] J. A. Russer and P. Russer, “An efficient method for computer aided analysis of noisy electromagnetic fields,” in IEEE MTT-S International Microwave Symposium Digest,. IEEE, Jun. 2011, pp. 1–4. [15] ——, “Network methods applied to the computation of stochastic electromagnetic fields,” in 2011 Int. Conf. on Electromagnetics in Advanced Applications (ICEAA). IEEE, Sep. 2011, pp. 1152–1155. [16] J. A. Russer, G. Scarpa, P. Lugli, and P. Russer, “On the modeling of radiated EMI on the basis of near-field correlation measurements,” in European Microwave Conference (EuMC), Manchester, 2011, pp. 9–12. [17] A. Baev, A. Gorbunova, M. Konovalyuk, Y. Kuznetsov, and J. A. Russer, “Planar stochastic sources localization algorithm in EMC problems,” in International Conference on Electromagnetics in Advanced Applications (ICEAA), 2013. IEEE, 2013, pp. 440–443. [18] A. Gorbunova, A. Baev, M. Konovalyuk, Y. Kuznetsov, and J. A. Russer, “Stochastic EMI sources localization algorithm based on time domain planar near-field scanning,” in Int. Symposium on Electromagnetic Compatibility (EMC EUROPE). IEEE, 2013, pp. 972–976. [19] D. Thomas, C. Obiekezie, S. Greedy, A. Nothofer, and P. Sewell, “Characterisation of noisy electromagnetic fields from circuits using the correlation of equivalent sources,” in 2012 International Symposium on Electromagnetic Compatibility (EMC EUROPE), Sep. 2012, pp. 1–5. [20] J. Russer, T. Asenov, and P. Russer, “Sampling of stochastic electromagnetic fields,” in IEEE MTT-S International Microwave Symposium Digest (MTT), 2012, Jun. 2012, pp. 1–3. [21] M. Krumpholz and P. Russer, “A field theoretical derivation of TLM,” Microwave Theory and Techniques, IEEE Transactions on, vol. 42, no. 9, pp. 1660–1668, 1994. [22] M. Krumpholz, C. Huber, and P. Russer, “A field theoretical comparison of FDTD and TLM,” Microwave Theory and Techniques, IEEE Transactions on, vol. 43, no. 8, pp. 1935–1950, 1995. [23] J. A. Russer, O. Fielonik, G. Scarpa, and P. Russer, “Modelling of noisy em field propagation using correlation information of sampled data,” in IEEE Int. Conf. on Numerical Electromagnetical Modeling and Optimization NEMO2014, Pavia, Italia, May 14–16 2014. [24] J. Russer and P. Russer, “Modeling of noisy em field propagation using correlation information,” to be published, 2015.
