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Passivity: Test, Visualize, and Enforce Passivity of Rational Fit Output

This example shows how to test, visualize, and enforce the passivity of output from the rationalfit function.

S-Parameter Data Passivity

Time-domain analysis and simulation depends critically on being able to convert frequency-domain S-parameter data into causal, stable, and passive time-domain representations. Because the rationalfit function guarantees that all poles are in the left half plane, rationalfit output is both stable and causal by construction. The problem is passivity.

N-port S-parameter data represents a frequency-dependent transfer function H(f). You can create an S-parameters object in RF Toolbox™ by reading a Touchstone® file, such as passive.s2p, into the sparameters function.

You can use the ispassive function to check the passivity of the S-parameter data, and the passivity function to plot the 2-norm of the N x N matrices H(f) at each data frequency.

S = sparameters('passive.s2p');
ispassive(S)
ans = logical
   1

passivity(S)

Figure contains an axes object. The axes object with title Data passive, max norm(H) is 1 - 4.06e-08 at 0.00054 GHz contains an object of type line.

Testing and Visualizing rationalfit Output Passivity

The rationalfit function converts N-port sparameter data, S into an NxN matrix of rfmodel.rational objects. Using the ispassive function on the N x N fit output reports that even if input data S is passive, the output fit is not passive. In other words, the norm H(f) is greater than one at some frequency in the range [0,Inf].

The passivity function takes an N x N fit as input and plots its passivity. This is a plot of the upper bound of the norm(H(f)) on [0,Inf], also known as the H-infinity norm.

fit = rationalfit(S);
ispassive(fit)
ans = logical
   0

passivity(fit)

Figure contains an axes object. The axes object with title F i t blank n o t blank p a s s i v e , blank H indexOf infinity baseline blank n o r m blank i s blank 1 blank + blank 1 . 7 9 1 e - 0 2 blank a t blank 1 7 . 6 8 1 6 blank G H z . contains 4 objects of type line.

The makepassive function takes as input an N x N array of fit objects and also the original S-parameter data, and produces a passive fit by using convex optimization techniques to optimally match the data of the S-parameter input S while satisfying passivity constraints. The residues C and feedthrough matrix D of the output pfit are modified, but the poles A of the output pfit are identical to the poles A of the input fit.

pfit = makepassive(fit,S,'Display','on');
ITER	 H-INFTY NORM	FREQUENCY		ERRDB		CONSTRAINTS
0		1 + 1.791e-02	17.6816  GHz	-40.4702
1		1 + 2.878e-04	275.337  MHz	-40.9167	5
2		1 + 9.291e-05	365.528  MHz	-40.9092	7
3		1 - 5.722e-07	368.237  MHz	-40.9061	9
ispassive(pfit)
ans = logical
   1

passivity(pfit)

Figure contains an axes object. The axes object with title F i t blank p a s s i v e , blank H indexOf infinity baseline blank n o r m blank i s blank 1 blank - blank 5 . 7 2 2 e - 0 7 blank a t blank 3 6 8 . 2 3 7 blank M H z . contains an object of type line.

all(vertcat(pfit(:).A) == vertcat(fit(:).A))
ans = logical
   1

Start makepassive with Prescribed Poles and Zero C and D

To demonstrate that only C and D are modified by makepassive, one can zero out C and D and re-run makepassive. The output, pfit still has the same poles as the input fit. The differences between pfit and pfit2 arise because of the different starting points of the convex optimizations.

One can use this feature of the makepassive function to produce a passive fit from a prescribed set of poles without any idea of starting C and D.

for k = 1:numel(fit)
    fit(k).C(:) = 0;
    fit(k).D(:) = 0;
end
pfit2 = makepassive(fit,S);
passivity(pfit2)

Figure contains an axes object. The axes object with title F i t blank p a s s i v e , blank H indexOf infinity baseline blank n o r m blank i s blank 1 blank - blank 3 . 8 0 8 e - 0 7 blank a t blank 3 6 2 . 9 2 6 blank M H z . contains an object of type line.

all(vertcat(pfit2(:).A) == vertcat(fit(:).A))
ans = logical
   1

Generate Equivalent SPICE Circuit from Passive Fit

The generateSPICE function takes a passive fit and generates an equivalent circuit as a SPICE subckt file. The input fit is an N x N array of rfmodel.rational objects as returned by rationalfit with an S-parameters object as input. The generated file is a SPICE model constructed solely of passive R, L, C elements and controlled source elements E, F, G, and H.

generateSPICE(pfit2,'mypassive.ckt')
type mypassive.ckt
* Equivalent circuit model for mypassive.ckt
.SUBCKT mypassive po1 po2
Vsp1 po1 p1 0
Vsr1 p1 pr1 0
Rp1 pr1 0 50
Ru1 u1 0 50
Fr1 u1 0 Vsr1 -1
Fu1 u1 0 Vsp1 -1
Ry1 y1 0 1
Gy1 p1 0 y1 0 -0.02
Vsp2 po2 p2 0
Vsr2 p2 pr2 0
Rp2 pr2 0 50
Ru2 u2 0 50
Fr2 u2 0 Vsr2 -1
Fu2 u2 0 Vsp2 -1
Ry2 y2 0 1
Gy2 p2 0 y2 0 -0.02
Rx1 x1 0 1
Cx1 x1 0 2.73023895517808e-12
Gx1_1 x1 0 u1 0 -2.05782979280167
Rx2 x2 0 1
Cx2 x2 0 7.77758887214204e-12
Gx2_1 x2 0 u1 0 -2.9156564056313
Rx3 x3 0 1
Cx3 x3 0 2.29141629549012e-11
Gx3_1 x3 0 u1 0 -0.544411439304171
Rx4 x4 0 1
Cx4 x4 0 9.31845201412272e-11
Gx4_1 x4 0 u1 0 -0.65447570357709
Rx5 x5 0 1
Cx5 x5 0 4.89917765731238e-10
Gx5_1 x5 0 u1 0 -0.0811448839382569
Rx6 x6 0 1
Fxc6_7 x6 0 Vx7 18.7462231906335
Cx6 x6 xm6 3.95175907326523e-09
Vx6 xm6 0 0
Gx6_1 x6 0 u1 0 -0.0922198693339307
Rx7 x7 0 1
Fxc7_6 x7 0 Vx6 -0.0837754714905208
Cx7 x7 xm7 3.95175907326524e-09
Vx7 xm7 0 0
Gx7_1 x7 0 u1 0 0.00772576303424427
Rx8 x8 0 1
Cx8 x8 0 1.25490425604427e-08
Gx8_1 x8 0 u1 0 -0.947657275176865
Rx9 x9 0 1
Cx9 x9 0 2.73023895517808e-12
Gx9_2 x9 0 u2 0 -2.08177258762568
Rx10 x10 0 1
Cx10 x10 0 7.77758887214203e-12
Gx10_2 x10 0 u2 0 -2.92596085173853
Rx11 x11 0 1
Cx11 x11 0 2.29141629549012e-11
Gx11_2 x11 0 u2 0 -0.607848165632945
Rx12 x12 0 1
Cx12 x12 0 9.31845201412272e-11
Gx12_2 x12 0 u2 0 -0.692626854180216
Rx13 x13 0 1
Cx13 x13 0 4.89917765731238e-10
Gx13_2 x13 0 u2 0 -0.0860860652409703
Rx14 x14 0 1
Fxc14_15 x14 0 Vx15 18.378751033687
Cx14 x14 xm14 3.95175907326523e-09
Vx14 xm14 0 0
Gx14_2 x14 0 u2 0 -0.0932006525386325
Rx15 x15 0 1
Fxc15_14 x15 0 Vx14 -0.0854505120387824
Cx15 x15 xm15 3.95175907326523e-09
Vx15 xm15 0 0
Gx15_2 x15 0 u2 0 0.00796404348177478
Rx16 x16 0 1
Cx16 x16 0 1.25490425604427e-08
Gx16_2 x16 0 u2 0 -0.948047147658724
Gyc1_1 y1 0 x1 0 -0.140471001007271
Gyc1_2 y1 0 x2 0 -0.0223148684901588
Gyc1_3 y1 0 x3 0 -1
Gyc1_4 y1 0 x4 0 -1
Gyc1_5 y1 0 x5 0 1
Gyc1_6 y1 0 x6 0 -1
Gyc1_7 y1 0 x7 0 -1
Gyc1_8 y1 0 x8 0 0.999784741857493
Gyc1_9 y1 0 x9 0 1
Gyc1_10 y1 0 x10 0 -1
Gyc1_11 y1 0 x11 0 0.809029720317582
Gyc1_12 y1 0 x12 0 0.941914484502725
Gyc1_13 y1 0 x13 0 -0.935152435336785
Gyc1_14 y1 0 x14 0 0.988828666092136
Gyc1_15 y1 0 x15 0 0.953993645369482
Gyc1_16 y1 0 x16 0 -1
Gyd1_1 y1 0 u1 0 0.60441948232728
Gyd1_2 y1 0 u2 0 -0.351263715990235
Gyc2_1 y2 0 x1 0 1
Gyc2_2 y2 0 x2 0 -1
Gyc2_3 y2 0 x3 0 0.899638365226551
Gyc2_4 y2 0 x4 0 0.99707784213448
Gyc2_5 y2 0 x5 0 -0.991689008594851
Gyc2_6 y2 0 x6 0 0.997599708651923
Gyc2_7 y2 0 x7 0 0.961905554376162
Gyc2_8 y2 0 x8 0 -1
Gyc2_9 y2 0 x9 0 -0.267060864184593
Gyc2_10 y2 0 x10 0 0.0690127465756914
Gyc2_11 y2 0 x11 0 -1
Gyc2_12 y2 0 x12 0 -1
Gyc2_13 y2 0 x13 0 1
Gyc2_14 y2 0 x14 0 -1
Gyc2_15 y2 0 x15 0 -1
Gyc2_16 y2 0 x16 0 0.999953867813877
Gyd2_1 y2 0 u1 0 -0.335914292655742
Gyd2_2 y2 0 u2 0 0.70134778079757
.ENDS

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