Die Analysis

Die analysis

Now we will run a die aggregation analysis using the device analyses we triggered in the device analysis notebook. Make sure all the analyses we triggered are finished (i.e. make sure the last cell in that notebook has finished running)!

As before, make sure you have the following environment variables set or added to a .env file:

GDSFACTORY_HUB_API_URL="https://{org}.gdsfactoryhub.com"
GDSFACTORY_HUB_QUERY_URL="https://query.{org}.gdsfactoryhub.com"
GDSFACTORY_HUB_KEY="<your-gdsfactoryplus-api-key>"

import getpass

from tqdm.auto import tqdm

import gdsfactoryhub as gfh

project_id = f"rings-{getpass.getuser()}"
client = gfh.create_client_from_env(project_id=project_id)
api = client.api()
query = client.query()

Die analysis

You can aggregate any metric for a die analysis, for example, we had 3 different ring radius. Each of which will have a different FSR.

from gdsfactoryhub.functions.die import aggregate_device_analyses

aggregate_device_analyses.run?

die_pkeys = [d["pk"] for d in query.dies().execute().data]
die_pkey = die_pkeys[0]

aggregate_device_analyses.run(
    die_pkey=die_pkey,
    device_data_function_id="fsr",
    output_key="fsr_mean",
    device_attributes={"radius_um": 20},
)

result = api.validate_function(
    function_id="aggregate_device_analyses",
    target_model="die",
    test_target_model_pk=die_pkey,
    file=gfh.get_module_path(aggregate_device_analyses),
    test_kwargs={
        "device_data_function_id": "fsr",
        "output_key": "fsr_mean",
        "device_attributes": {"radius_um": 20},
    },
)
result.summary_plot()

with gfh.suppress_api_error():
    result = api.upload_function(
        function_id="aggregate_device_analyses",
        target_model="die",
        file=gfh.get_module_path(aggregate_device_analyses),
    )

task_ids = []
for die_pk in (pb := tqdm(die_pkeys)):
    pb.set_postfix(die_pk=die_pk)
    task_id = api.start_analysis(  # start_analysis triggers the analysis task, but does not wait for it to finish.
        analysis_id=f"die_fsr_aggregation_r20um_{die_pk}",
        function_id="aggregate_device_analyses",
        target_model="die",
        target_model_pk=die_pk,
        kwargs={
            "device_data_function_id": "fsr",
            "output_key": "fsr_mean",
            "device_attributes": {"radius_um": 20},
        },
    )
    task_ids.append(task_id)

# this cell waits for the last triggered analysis to finish.
# please only continue with the wafer analysis notebook after this cell ran.
result = api.wait_for_result(task_ids[-1])
result.summary_plot()
Device Analysis Wafer Analysis
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