Couckuyt, Ivo

Dhaene, Tom

Gazda, C

Pues, H

Rogier, Hendrik

Stijnen, K

Vande Ginste, Dries

English

Ghent University Academic Bibliography

An Immunity Modeling Technique to Predict theInfluence of Continuous Wave and AmplitudeModulated Noise on Nonlinear Analog CircuitsCelina Gazda, Dries Vande Ginste, Hendrik Rogier,Ivo Couckuyt and Tom DhaeneDepartment of Information TechnologyGhent UniversityGent, BelgiumEmail: celina.gazda@intec.UGent.beKristof Stijnen and Hugo PuesMelexis Technologies N.V.Tessenderlo, BelgiumAbstract—An innovative method to model, in the early designstage, the behavior of nonlinear analog circuits in the presenceof noise is presented. The technique relies on Harmonic Balanceanalysis and the obtained model does not only efficiently predictthe influence of a continuous wave disturbance, it may alsobe used to quickly estimate the device’s behavior while beingsubjected to amplitude modulated noise. The method leveragessurrogate models, as such reducing the simulation time andconcealing the intellectual property of the circuit manufacturer.Moreover, the model can be easily integrated into a circuitsimulator. An industrial case study of a voltage regulator forautomotive applications is described in this contribution and itclearly confirms the capabilities of the method.I. INTRODUCTIONThe design of electronic circuits intended for the automotiveindustry is a challenging task, as these devices must beable to work in a noisy environment without suffering fromelectromagnetic compatibility (EMC) issues. Obviously, it isof utmost importance to guarantee proper EMC behavior ofelectronic circuits before they reach the market. To unify thevalidation methods, international EMC standards have beendeveloped, providing clear definitions, exact testing proce-dures and limitations to the allowed levels of disturbanceand susceptibility. However, the typical approach to verify acircuit’s compliance with the EMC standards in its test oreven production phase, often results in expensive redesigns andsignificantly lengthens the total time-to-market. To avoid this,efficient modeling techniques are needed that help engineersto mimic the EMC behavior of the circuit in its early designstage, giving the possibility to efficiently remedy the problems.In this contribution we propose a novel method to constructa behavioral model, which allows to predict the performanceof the electronic circuit subjected to a standardized immunitytests, such as the direct power injection (DPI) test. Using an in-dustrial case study of a nonlinear analog voltage regulator, it isshown that the newly developed Harmonic Balance surrogate-based model is perfectly suited for modeling the impact ofthe continuous wave (CW) testing signals. Moreover, it alsoallows to quickly estimate the circuit’s behavior when am-plitude modulated (AM) noise is injected. As the constructedimmunity model consists of surrogates, it provides a very shortsimulation time, while maintaining high accuracy and, veryimportantly, it hides the original netlist of the modeled circuit.The complete methodology was first detailed in [1] for CWnoise only. This contribution demonstrates how to employ theobtained behavioral model to also predict the influence of AMnoise on the behavior of the device.In the next section we briefly describe how the DPI testis performed, using CW and AM testing signals. Section IIIpresents the developed modeling technique. Section IV showsthe CW DPI test results obtained using the proposed behav-ioral model and explains how to apply the model to alsopredict the AM DPI test. The last section summarizes ourwork.II. DIRECT POWER INJECTION TESTDCsupply- +DUTRSMAoptionaldecouplingcapacitor CdCLmonitoringequipmentbias teeFig. 1. Schematic configuration of the DPI test set-upFig. 1 depicts a schematic set-up of a DPI test, whichis a popular immunity test that allows to validate the EMCbehavior of an electronic circuit subjected to conducted radiofrequency (RF) noise. To perform it under standardized con-ditions, we follow the 62132-4 standard of the InternationalTechnical Commission (IEC) [2]. Therefore, the device-under-test (DUT), i.e. the IC, is placed on a printed circuit board(PCB), together with all the necessary components requiredfor the proper functioning of the DUT. Via a bias tee, atesting signal, i.e. RF noise, is injected into the DUT and itsVAMpeak VCWpeak CW noise AM noise Fig. 2. Peak preservation rule between continuous wave (CW) and amplitudemodulated (AM) signal.performance is being monitored, using a decoupling networkwith a very high impedance, represented in Fig. 1 by R. Thefrequency of the RF noise is swept from 150 kHz to 1 GHz andits power is varied between 0 dBm and 30 dBm. The maximalpower levels at which the observed characteristics of the DUTremain within preset specifications are recorded. It is assumedthat the DUT passes the test only if it can withstand 30 dBmof RF noise over the complete frequency range. Otherwise,the circuitry of the DUT must be adapted or extra precautions,such as for example ferrite beads or decoupling capacitors Cd,must be added to improve the EMC behavior.The IEC 62132-4 DPI test specification states that theinjected RF noise can be in the form of a continuous wave(CW)signal or that it can be amplitude modulated (AM). How-ever, if the AM signal is applied, the peak power PAMpeak, andthus also the corresponding peak voltage VAMpeak (Fig. 2),should remain the same as in case of CW:PAMpeak = PCWpeak, (1)VAMpeak = VCWpeak. (2)Therefore, the correlation between the mean power of the CWand the AM RF noise used in the DPI test, as shown in AnnexB of the ISO11452-1 standard concerning road vehicles [3],can be expressed as:〈PAM 〉 = 〈PCW 〉 · 2 +m22(1 +m)2, (3)where m is the modulation index and 〈·〉 indicates the mean.By default it is recommended to perform the modulation usinga modulating signal with frequency fmod = 1 kHz and withm = 0.8. Substituting these values into (3), yields 〈PAM 〉 =0.407〈PCW 〉.III. IMMUNITY MODELING - CASE STUDYSince the CW DPI test is more severe for the DUT in termsof average injected power, we first focused on developinga modeling technique that allows a reliable prediction ofthe results of this test. As a case study, we used a non-linear voltage regulator (VR), called MLXTC883, designedby Melexis Technologies N.V., Belgium, which consists ofintegrated active nonlinear components, i.e. 21 transistors,and 123 passive components (resistors and capacitors). In itsintended automotive application, the VR MLXTC883 receives5V DC on its supply pin, which it converts into a stable 3.3VDC on its output. However, it is found that, for a large rangeof noise frequencies and for sufficiently high noise amplitude,the desired 3.3 V output voltage drops significantly.Iin(t)Vin(t)Vout(t)voltage regulatororiginal netlist(SPICE, Spectre, ...)Fig. 3. Schematic used for Harmonic Balance simulation of the voltageregulator.To effectively model this nonlinear behavior, first, we collectall necessary data by means of Harmonic Balance (HB)simulations of the VR’s original netlist. We use this simulationmethod, because it yields frequency-domain data by directlycalculating the steady-state spectral content of voltages andcurrents in the circuit, thus it is perfectly suited to analyzenonlinear analog circuits. Moreover, HB simulation is muchfaster than transient tests, hence we can significantly reduce thetotal modeling time. We perform the simulations in Agilent’sAdvanced Design System (ADS), using the circuit schematicpresented in Fig. 3. The custom-created block, containing thecomplete netlist of the investigated VR, is connected to avoltage source, which produces a waveform:Vin(t) = Vin,CW (t) ≡ Vin,DC+Vin,CW sin(2pifnoise t). (4)This signal consists of the 5V DC component Vin,DC thatrepresents the DC supply of the MLXTC883 VR, and theRF component, being the CW RF noise at frequency fnoiseand amplitude Vin,CW . As our research goal is to investigatethe influence of RF noise on the performance of this VR,the DC input voltage Vin,DC is fixed. By varying fnoise andVin,CW , a complete analysis of the circuit’s immunity behavioris obtained, and a parameterized model can be constructed.To generate an accurate immunity model of the MLXTC883VR, the DC current and the first harmonic at the inputare recorded (tests proved that higher order harmonics arenegligible, for further details see [1]), approximating the totalinput current as:Iin(t) ≈ Iin,DC(fnoise, Vin,CW )+ Iin,CW,1(fnoise, Vin,CW ) sin(2pifnoise t). (5)The nonlinear behavior of the VR is characterized in (5) bythe dependance of the DC component Iin,DC and the firstharmonic Iin,CW,1 on the CW noise frequency and amplitude.From the input voltage and the input current, for both theDC and the first harmonic, we define the corresponding inputimpedances as follows:Rin,DC(fnoise, Vin,CW ) =Vin,DCIin,DC(fnoise, Vin,CW ), (6)Zin,CW,1(fnoise, Vin,CW ) =Vin,CWIin,CW,1(fnoise, Vin,CW ). (7)These two impedances can make sure that for a given inputsignal (9), the current Iin flowing into the VR remains cor-rect, accurately mimicking the circuit’s behavior during DPItest conditions. Therefore, these impedances constitute twoessential building blocks of the developed immunity model.The third and last component of the model represents the DCoutput voltage, approximated as:Vout(t) ≈ Vout,DC(fnoise, Vin,CW ), (8)and it models the nonlinear response of the voltage regulatorin the presence of the injected CW RF noise. It is sufficientto consider only the DC voltage at the output of the VR,as the circuit, thanks to its low-pass filtering characteristics,suppresses the RF signals at its output [1]. Additionally, weconsider the output pin as being open, because in its typicalautomotive application, as well as during the DPI test, thispin is loaded with a very high impedance. Thus, the outputimpedance, does not have an impact on the behavior of the VRand therefore it is not included in the model. All the parametersand assumptions explained above lead to the equivalent modelarchitecture depicted in Fig. 4. To speed-up the simulationprocess and to hide the intellectual property of the circuit’smanufacturer, while still maintaining high accuracy, we repre-sent these three crucial model components Rin,DC , Zin,CW,1and Vout,DC by surrogates [4], more specifically by artificialneural networks (ANNs) [5], [6]. A detailed description ofhow the surrogates were created is given in [1].Rin,DC(fnoise, Vin,CW ) Zin,RF,1(fnoise, Vin,CW )Vout,DC(fnoise, Vin,CW )RF block DC blockVin VoutFig. 4. Architecture of the voltage regulator’s behavioral model, illustratingthe three pertinent components (building blocks).IV. RESULTSA. CW testing signalWe now integrate the model of Fig. 4 back into Agilent’sADS circuit simulator. This is a straightforward operation,as all three components are ANNs, which are merely mathe-matical functions. An exemplary comparison of the Vout,DCcharacteristics obtained from the HB simulations using theoriginal VR netlist (red line) on the one hand and from thesurrogate model (green line with squares) on the other hand, ispresented in Fig. 5. In this case, the frequency fnoise is fixedto 30 MHz and the amplitude of the CW RF noise Vin,CW isswept from 0.32 to 10 V, which corresponds to an injectionof CW RF noise with power increasing from 0 to 30 dBmin a 50 Ω load system, as reqiired by the specifications givenin [2]. Very good agreement between both results is obtained,which proves that our surrogate-based model can accuratelypredict the influence of the CW RF noise on the investigatedVR. The total time needed to obtain results from Fig. 5 byemploying the circuit’s original netlist equals 70.15 s, whereasfor the immunity model it only takes 1.8 s, resulting in aspeed-up factor of 39. This clearly indicates the efficiency ofour method.Fig. 5. Validation of the immunity modeling approach:Vout,DC(fnoise, Vin,CW ) at fnoise = 30 MHz.To further test our immunity model, we integrate it intoa complete setup of the DPI test, specified in [2], whichprinciple schematic was presented in Fig. 1. The block repre-senting the VR (its original netlist or the behavioral model)is now first connected to 1 nH inductors that mimic therole of the VR’s package. Next, by adopting the EM/circuitco-simulation technique described in [7], the effect of thePCB is included. Thereto, using full-wave simulation obtainedwith ADS-Momentum, the pertinent scattering parameters ofthe unpopulated PCB are imported into the circuit solver,where they are combined with the other components, i.e.the packaged VR and the necessary lumped elements. A DCblocking capacitor capacitor AVX Z5U 08055E223MAT2Awith a nominal value C = 22 nF, together with DC feedinginductor (Ferroperm Type 1583 RF choke) with a nominalvalue L = 47 µH constitute the bias tee. The choice ofthese lumped components, including their parasitic effects, iscarefully made, as advised in [7], so that the requirementsconcerning the pertinent RF injection path given in [2] arefulfilled. The 5V DC supply is provided from a DC voltagesource with a DC supply capacitor GCM1885C1H331JA16of 330pF, and the RF noise is generated by an RF powergenerator. The behavior of the VR, i.e. its output DC voltage, ismonitored using a resistor of R = 1 kΩ as decoupling network.Fig. 6. Comparison of the CW DPI test results of the original circuit andthe surrogate-based behavioral model.Fig. 6 shows the results of the simulated DPI test using boththe original netlist (red line) and the surrogate-based model(green line with circles). These curves represent the maximumvalue of the CW RF noise power Pnoise (in dBm) that theinvestigated VR can withstand while Vout,DC still remainswithin an acceptable ±100 mV margin from its desired value,i.e. Vout,DC ∈ [3.2V, 3.4V]. Over the complete frequencyrange of the simulated DPI test, very good agreement betweenboth results is observed. At 150 kHz, according to the originalnetlist, the IC withstands 19 dBm and still functions correctly,whereas the immunity model returns a value of 18 dBm.For the broad frequency range from 500 kHz till 100 MHz,the VR cannot withstands more than 14 dBm of CW RFnoise. For frequencies higher than 300 MHz, the IC fullypasses the DPI test, as it is capable to withstand 30 dBmof CW RF noise. The simulation time for this test using theoriginal netlist equals 2913.8 s, whereas for the same testperformed by relying on the surrogate-based model only 32.6 sare needed. Therefore, by applying the proposed behavioralmodel, a significant speed-up factor of 90 is obtained.B. AM testing signalNow we demonstrate how the developed immunity modelallows to quickly estimate the influence of an AM testingsignal on the circuit. As an example of the injected AM RFnoise, we use the signal presented in Fig. 7(a) (detailed viewin Fig. 7(b)), which has the waveform:Vin(t) = Vin,AM (t) ≡ Vin,DC (9)+Vin,CW(1 +m)(1 +m cos(2pifmod t)) cos(2pifnoise t),where Vin,DC is the 5 V DC supply, the modulating indexm = 0.8, the modulating frequency fmod = 1 kHz, the noisefrequency fnoise is equal to 30 MHz, and the peak amplitudeof the AM RF noise is equal to 10V. This peak amplitudecorresponds to the requirement put forward in Section II, andit is equivalent to the maximum CW RF noise of 30 dBm.Note also, from Fig. 6, that the VR performs the worst atthis noise frequency of 30 MHz. First, a transient simulationVenv(tn) Fig. 7(b) (a)(b)Vout,DC(tn) (c)Fig. 7. AM RF noise injection (a) Vin(t) (b) zoomed view of Vin(t) (c)Vout(t).was leveraged to analyze the influence of this AM RF signalon the output voltage. This simulation took 84 min 32 s. Thisvery long simulation time can be attributed to the multi-scaleproblem we are dealing with. Indeed, the rapidly oscillatingcarrier signal requires many testing samples (using classicalsimulation techniques, the sampling frequency should be atleast twice the frequency of the sampled signal) and the slowlyvarying modulating signal requires a long signal duration.To expedite the simulations we propose to use the surrogate-based immunity model again, but this time to reconstruct thetime-domain waveform. Thereto, first, we collect n samplesfrom the envelope Venv(t) of the AM RF noise. This envelopeis marked with a black line in Fig. 7(a) and the collectedsamples are indicated by the black squares. Second, eachenvelope sample, denoted Venv(tn), is translated into an outputsample Vout,DC(tn). This is done by using Venv(tn), i.e. theamplitude of the modulation, and the frequency fnoise of thecarrier (here 30 MHz) as input for our immunity model ofFig. 5. By collecting all the Vout,DC(tn) values, marked inFig. 7(c) with black squares, we can quickly reconstruct theVout(t) signal. A good agreement with the results from thetransient simulation of the original netlist (red line) is obtained.Furthermore, the total simulation time of the novel approach isof the order of seconds (depending on the number of samples).In future work we will investigate to what extent this methodto efficiently reproduce these kind of time-domain waveformscan be extended to general analog circuits.V. CONCLUSIONA surrogate-based immunity modeling technique, whichallows to predict the behavior of electronic devices duringimmunity testing, has been proposed. The model is constructedstarting from Harmonic Balance simulations and a propermodel architecture was put forward. By replacing the pertinentcomponents of the developed model with surrogates, themodel hides the original netlist of the circuit and it is easilyintegrated into commercial software. Moreover, the simulationtime becomes very short.In this contribution, an industrial case study, being a non-linear analog voltage regulator for automotive applications,was selected. First, the immunity model was validated bymimicking the DPI test (IEC62132-4), during which a CW RFnoise signal is injected into the DUT. Very good agreementbetween the results obtained with the original netlist and withthe novel immunity model were presented. Second, it wasshown that the model can also be leveraged to mimic thebehavior of the VR whilst being subjected to an AM RFnoise signal. Apart from the good agreement, compared tothe transient simulations of the original netlist, a considerablespeed-up was obtained.REFERENCES[1] C. Gazda, D. Vande Ginste, H. Rogier, I. Couckuyt, T. Dhaene, K. Stijnen,and H. Pues, “Harmonic Balance surrogate-based immunity modeling ofa nonlinear analog circuit,” accepted for publication in IEEE Transactionson Electromagnetic Compatibility, vol. -, pp. –, 2013.[2] Integrated Circuits - Measurement of Electromagnetic Immunity, 150 kHzto 1 GHz - part 4: Measurement of Conducted Immunity - direct RFpower injection method., International Electrotechnical Commission, IEC62132-4 Std., 2006.[3] Road Vehicles - Component Test Methods for Electrical Disturbances fromNarrowband Radiated Electromagnetic Energy Part 1: General Princi-ples and Terminology, International Organization for Standardization, ISO11452-1 Std., 2005.[4] M. Yelten, T. Zhu, S. Koziel, P. Franzon, and M. Steer, “Demystifyingsurrogate modeling for circuits and systems,” IEEE Circuits and SystemsMagazine, vol. 12, no. 1, pp. 45–63, Feb. 2012.[5] J. Lawrence, Introduction to Neural Networks, 5th Edition. CaliforniaScientific, Nevada City, 1993.[6] D. Gorissen, L. De Tommasi, K. Crombecq, and T. Dhaene, “Sequentialmodeling of a low noise amplifier with neural networks and activelearning,” Neural Comput. Appl., vol. 18, no. 5, pp. 485–494, Jun. 2009.[7] D. Vande Ginste, H. Rogier, D. De Zutter, and H. Pues, “Efficient analysisand design strategies for radio frequency boards dedicated to integritymonitoring of integrated circuits using an electromagnetic/circuit co-design technique,” IET Science, Measurement Technology, vol. 4, no. 5,pp. 268–277, Sep. 2010.

An immunity modeling technique to predict the influence of continuous wave and amplitude modulated noise on nonlinear analog circuits

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An immunity modeling technique to predict the influence of continuous wave and amplitude modulated noise on nonlinear analog circuits

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