eldo_user

Statistical Experimental Design and Analysis
Statistical Modeling for Discrete Circuits
These points, which are not necessarily the true worst case points, are systematically tested
when the selected screening design uses about 2N runs (this is the default argument of the
DESIGN argument of the .DEX command).
Related Topics
Factor Screening Experiments in Eldo
Statistical Modeling for Discrete Circuits
Statistical Modeling for Discrete Circuits
When screening out the important main effects for discrete RLC circuits you can safely
investigate only the main effects associated with the noise factors.
Consider the following netlist (extracted from the example filter.cir available in the directory
$MGC_AMS_HOME/examples/dex/):
V1 1 0 DC 0 AC 1
R1 1 2 ’R1 + D_R1’
R3 2 0 ’R3 + D_R3’
C2 2 INN ’C + D_C’
C3 2 OUT ’C + D_C’
R2 OUT INN ’R2 + D_R2’
Y1 OPAMP1 0 INN OUT 0 PARAM: GAIN=’GAIN + D_GAIN’ P1=’P1 + D_P1’
where the circuit parameters are defined as follows:
* DETERMINISTIC PARAMETERS
.PARAM R1 = 159K R2 = 3.18MEG R3 = 79.5
+ C = 1U
+ P1 = 10 GAIN = 10E6
* NOISE PARAMETERS
.PARAM D_R1 = AGAUSS( 0, 10.6K, 1)
.PARAM D_R2 = AGAUSS( 0, 0.212MEG, 1)
.PARAM D_R3 = AGAUSS( 0, 5.30, 1)
.PARAM D_C = AGAUSS( 0, 0.067U, 1)
.PARAM D_P1 = AGAUSS( 0, 0.667U, 1)
.PARAM D_GAIN = AGAUSS( 0, 0.667E6, 1)
For the resistors, the statistical model is expressed by transformations of the form R + D_R
where R represents the nominal value and D_R is the element tolerance or the random
variations associated with the control parameter R. These random variables have zero mean.
This reflects an important property of discrete RLC circuits—that the controllable parameters x
and the statistical variables s are in the same space. In other words, any circuit performance is a
function of random expressions E(x i , s i ) = x i + s i .
This property implies that a circuit performance y(x, s) = y(x + s) satisfies the following
relation:
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Eldo® User's Manual, 15.3