Instrumentation & Measurement Magazine 25-5 - 58
process. Low copper conductivity
used during the
extraction of the model results
in large parasitics.
The VR bandwidth mismatch
can also affect the
third droop correlation. It
is important that the same
nodes are probed during
the lab measurement and
in simulation during the
correlation process.
Once the source of misFig.
5. Flow chart for server platform correlation.
to the PDN modeling, such as the copper conductivity used
during the modeling process. Using the wrong copper conductivity
numbers can result in either pessimistic or optimistic
simulation results as this affects the parasitics of the PDN.
Another source of miscorrelation is due to the decoupling
capacitors. The use of the right decoupling capacitance is necessary
to achieve good correlation. Derated capacitance based
on temperature, dc bias, end of life, and beginning of life must
be properly determined. It is also necessary to consider parameter
variations of the passive components. Once the source
of the miscorrelation is found, the simulation analysis is performed
again and compared with validation results. Once
correlation is achieved, there is confidence in using both the
PDN and VR models for predicting the platform response to
different scenarios.
Correlation and Prediction Examples
Fig. 6 shows an example of a server platform that is poorly
correlating with lab measurements. In this case, the voltage
droop falls outside the tolerance band obtained during
infinite persistence mode. In this example, the VRTT is
connected to the interposer and serves as the load and the
voltage measurement is taken at the VR sense point. There
are major discrepancies in the voltage droop and overshoot
between the lab measurements and simulation results with
the modeled server platform PDN. These discrepancies can
be narrowed down to load current slew rate mismatch, a
much more aggressive slew rate will result in larger droop
events. The discrepancies can also be due to low copper conductivity
values used during platform PDN model extraction
58
correlation is identified,
the process is repeated
to get a good correlation.
An example of good correlation
is shown in Fig.
7. This shows the voltage
droop and overshoot between
simulation and lab
measurement for two iterations
of the models.
The first iteration shows
poor correlation due to
the PDN parasitics not
being adequately modeled. This is resolved in the second
iteration which shows good correlation within a few mV.
The transient response of the second model iteration lies
within the tolerance band captured during infinite persistence
mode. The voltage droop and overshoot are of great
importance as they determine whether the PDN is adequately
designed. Once good correlation is achieved, there
is enhanced confidence in the VR and platform PDN models.
This also enhances confidence for predicting the server
platform behavior in response to various workloads and VR
solution and motherboard components changes, without
having to redesign and manufacture new boards.
An example of using the correlated models based on measurements
to predict performance using a load current at 5
kHz, 70% duty cycle is shown in Fig. 8. The maximum and
minimum voltages error between simulation and lab measurements
are less than 1%. This goes to show that when good
correlation is done, the models used in the correlation process
can be used to predict performance of the system under various
loading conditions.
Server platforms that exist in datacenters need to be designed
efficiently at a reduced cost and improved time to
market due to their increasing importance in the age of processing
zettabytes of data. To do that, methodologies need to
be developed to create robust platform power delivery models
that can be used to quickly perform 'what if' analysis and
predict server power delivery performance behavior. This can
be achieved by leveraging measurement, simulation, and correlation
through modeling and laboratory validation. With
enhanced confidence in robust models, quick design guidance
IEEE Instrumentation & Measurement Magazine
August 2022
Instrumentation & Measurement Magazine 25-5
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