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The SIMetrix environment provides a facility to run automatic multiple SIMPLIS analyses. Two modes are available namely parameter step and Monte Carlo.
In parameter step mode, the run is repeated while setting a parameter value at each step. The parameter may be used within any expression to describe a device or model value.
In Monte Carlo mode runs are simply repeated the specified number of times with random distribution functions enabled. Distribution functions return unity in normal analysis modes but in Monte Carlo mode they return a random number according to a specified tolerance and distribution. Any model or device parameter may be defined in terms of such functions allowing an analysis of manufacturing yields to be performed.
The multi-step analysis modes offered in SIMetrix simulation mode achieve the same end result as the SIMPLIS multi-step modes but their method of implementation is quite different.
SIMetrix multi-step analyses are implemented within the simulator while the SIMPLIS multi-step analyses are implemented by the front end using the scripting language. The different approaches trade off speed with flexibility. The approach used for SIMPLIS is more flexible while that used for SIMetrix is faster.
We will begin with an example and will use one of the supplied example schematics. First open the schematic Examples/SIMPLIS/Manual_Examples/Example1/example1.sxsch. We will set up the system to repeat the analysis three times while varying R3. Proceed as follows:
| {r3_value} |
The analysis will be repeated three times for values of r3_value of 20, 60 and 100. The resistor value R3 is defined in terms of r3_value so in effect we are stepping R3 through that range.
In most cases you will probably want to step just one part in a similar manner as described above. But you can also use the parameter value to define any number of part or model values.
If you now run a normal single analysis, you will find that SIMPLIS reports an error as it is unable to resolve the value for R3. This can be overcome by specifying the value using a .VAR control. Add this line:
| .VAR r3_value=100 |
to the F11 window. This line defines the value of R3 when a normal single step analysis is run.
The above example illustrates a linear multi-step parameter run. You can also define a decade (logarithmic) run and also a list based run that selects parameter values from a list. To set up a list run, select the List radio button, then press Define List... Enter the values for the list using the dialog box.
The Group Curves check box controls how graphs are displayed. If unchecked, curves for each run will have their own legend and curve colour. If checked, curves will all have the same colour and share a single legend.
To set up a Monte Carlo analysis. you must first define part tolerances. This is done by defining each value as an expression using one of the functions Gauss(), Unif or WC(). Here is another example. Open the same example circuit as above then make the following changes:
| {100*GAUSS(0.05)} |
| {100u*GAUSS(0.2)} |
If your system and your license permit, you can specify multiple processor cores to be employed for multi-step analyses. This can substantially speed up the run. For example, suppose you have a 4 core system and wish to run a 100 step Monte Carlo analysis. The 100 steps may be split across the 4 cores each doing 25 steps each. In most cases this improve the run time by at least a factor of 3.
In this mode of operation, multiple SIMPLIS processes run independently with each one creating its own data file.
Setup the multi-step analysis as normal. In the Define SIMPLIS Multi Step Analysis dialog box set the Number of cores to the desired value as shown below. Note that the maximum value you can set is determined by your license and system. For allowed number of cores for each product type, see Simulation and Multi-core Processors.
Start the run in the usual way for a multi-step run. You will see the usual SIMPLIS status box but with the difference that there will be multiple tabs, one for each process running as shown below:
You can watch the progress of each process using the tabs at the top of the status box.
Fixed probes usually update the waveform viewer incrementally. That is, the display is updated while the simulation proceeds. This does not happen for multi-core multi-step runs. Instead, the graphs will be updated when the run is complete.
Tolerances are defined using distribution functions. For SIMPLIS Monte Carlo there are just three functions available. These are defined below.
| Function Name | Description |
|---|---|
| GAUSS(tol) | Returns a random with a mean of 1.0 and a standard deviation of tol/3. Random values have a Gaussian or Normal distribution. |
| UNIF(tol) | Returns a random value in the range 1.0 +/- tol with a uniform distribution |
| WC(tol) | Returns either 1.0-tol or 1.0+tol chosen at random. |
No special provision has been made to implement so called 'Lot' tolerances which model tolerances that track. However, it is nevertheless possible to implement Lot tolerances by defining a parameter as a random variable. Suppose for example that you have a resistor network consisting of 4 resistors of 1k with an absolute tolerance of 2% but the resistors match to within 0.2%. The absolute tolerance is the 'lot' tolerance. This is how it can be implemented:
| .VAR rv1 = {UNIF(0.02)} |
| {1K * rv1 * UNIF(0.002)} |
When a Monte Carlo analysis is run a log file is created which details which device values were changed and what they were changed to. Two versions of the log file are created; one is plain text and is called simplis_mclog.log and the other is in HTML format and called simplis_mclog.html.
To view the HTML log file, select menu
An example is shown below:
The random values used in Monte Carlo analysis are generated using a pseudo-random number generator. This generates a sequence of numbers that appear to be random and have the statistical properties of random numbers. However, the sequence used is in fact repeatable by setting a defined seed value. The sequence generated will always be the same as long as the same starting seed value is used.
This makes it possible to repeat a specific Monte Carlo step as long as the seed value is known. SIMetrix/SIMPLIS explicitly sets a random seed value at the start of each Monte Carlo run and records this in a file. A facility exists to explicitly set the seed value instead of using a random seed and this makes it possible to repeat a specific step. This is useful in cases where one or more Monte Carlo steps show unexplained behaviour and further investigation is needed.
This is also useful if you wish to find the values of the parts used for a particular Monte Carlo step.
To find the seed value of a particular step, follow this procedure:
You can use the seed value to repeat the run associated with it. Follow this procedure:
The part values used for a particular step can be found by studying the .deck file that is created for every SIMPLIS run. To do this, follow the procedure for finding and using the seed value as described above. Then after the single run is complete locate the file with the .deck extension. This will be located in the SIMPLIS_DATA folder and will have a name the same as the top level schematic but with the extension .deck. So if your schematic is called DC-DC-Converter.sxsch, the deck file will be called DC-DC-Converter.deck and will be located in the SIMPLIS_DATA folder.
The .deck file is an ASCII file that list the parts in the design with their connections and values. Its format is similar to a SPICE netlist. The picture below illustrates the process of matching a part in the schematic to an entry in the .deck file:
In the above diagram, note the line for C5 shows a value of 2.2797033192142e-008 in the .deck file
Once a SIMPLIS multi-step or Monte Carlo analysis is complete, the data can be analysed in exactly the same way as for SIMetrix multi-step analyses. This includes the performance analysis and histogram features. For more information, see Performance Analysis and Histograms.
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