SAW Unit Cell Monte Carlo Study

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Monte Carlo Simulation a statistical method which uses simulation to model the probability of outcomes of a complex model whose behaviour cannot be easily determined due to a vast number of variables. Filter performance is affected by a number of design variables.

In this example we vary both the finger pitch, finger width and electrode thickness to get a better idea of the design space and how the key performance (KPIs) relate to these inputs.

Model Setup

A schematic of the model and the three input variables can be seen below:

model.png

The key model parameters were as follows. finger pitch, finger width and electrode thickness are set as design variables to be randomly varied (indicated by *).

Design Variable Description Default Value
subs_thk Substrate thickness 7.5 um
elec_thk* Electrode thickness 200 nm
fin_pitch* Finger pitch 1.3 um
aratio Aspect ratio 0.5
fin_width* Finger width 65 um
fin_gap Finger gap 0.65 um
nfing  Number of fingers 100
fin_len Finger Length 50 um
cut_ang Rotated Y-cut angle 42

1000 random input variables were created with the following constraints:

  • Finger pitch: 1.3 um ± 12.3% 
  • Finger width: 65 um ± 0.3%
  • Electrode thickness: 200 nm ± 0.3%

Monte Carlo Results

The full study was completed in 12 minutes when using 2 cores per simulation and had a total cost of 36.28 Core-Hours.

Using the outputs from the simulations, it is possible to calculate the KPIs such as resonant frequencies and Q. The inputs and outputs can then be plot in a number of ways to get insight into the device performance. Results are plotted below in MATLAB.

dist1.png
Input autocorrelation
dist2.png
KPI autocorrelation
dist3.png
Correlation between inputs and KPIs

Try this Yourself

 To run this Monte Carlo study you will need to download the OnScale and MATLAB files. 

Download: Monte Carlo Files

  1. Extract all of the files from the downloaded folder
  2. Open 'monte_carlo_pre_v1.m' and select run
  3. Open OnScale and Select Cloud Scheduler 
  4. Select 'saw_unit_3D.flxinp' as the input file
  5. Under Parametric Sweep, select User Defined Variable File from the dropdown
  6. Next to Input Files, select ... and open 'simdata.csv'
  7. Select Estimate + Run
  8. Download all *.flxhst files 
  9. Open 'mat_impd.revinp'
  10. Insert the name of the directory which your files were downloaded to into the variable tdir
  11. Select Run
  12. Open 'monte_carlo_post_v1.m' and select Run

 

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