grating_coupler QA

Note: For legacy data format (2022 R2.4 and older) please visit following link:

grating_coupler QA (Legacy)

Photonic Model: grating_coupler

Information on QA tests and QA types: Introduction to Quality Assurance (QA) tests

Instructions on running QA tests: Running QA tests in CML Compiler

QA tests

QA Variables 

These variables are defined as below in a struct named "QA" in the source data file: 

[[snippet||12327089469331]]

Statistical QA Variables 

(statistical compact models only) 

These variables are defined as below in a struct named "QA" in the "statistical" struct in the source data file: 

[[snippet||12328282179731]]

QA Scripts

grating_coupler_dpeakwl_dT_qa

grating_coupler_IL_R_qa

grating_coupler_peak_wl_BW_skew_qa

This script sets up the following test-bench. The ONA is set to do analysis in "scattering data" mode for frequency domain simulations and "impulse response" for time domain simulations. 

The scripts read the result of ONA and extract the FOMs. The FOMs for the main polarization are compared to peak_IL_dB, R1_dB, R2_dB, peak_wavelength_mainpol, bandwidth_1dB_mainpol, skew_mainpol and dpeakwavelength_dT_mainpol provided in the source data and made sure the difference is within the tolerance. Also these insertion loss and reflection are extracted in time domain simulation and results are compared to frequency domain simulations and made sure the difference is less than time tolerance value.

grating_coupler_statistical_qa.lsf

In this test, for each FOM the Monte Carlo analysis is performed on the frequency domain test-bench as  described above and data is compared to data saved in "<element_name>_stat_refdata.mat". 

This test is a regression test. If this is the first time running QA, the "reference_data_available" in source data should be set to "false", so the Monte Carlo results from first run will be saved in the element folder as  in "<element_name>_stat_refdata.mat". For consecutive runs, "reference_data_available" should be set to true, and results will be compared to these statistical data and make sure the difference is within the tolerance value.