Wafer Analysis

Wafer Analysis

Now we will run a die loss cutback analysis on the die data we uploaded earlier.

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

Lets Define the Upper and Lower Spec limits for Known Good Die (KGD).

For example:

waveguide width (nm) Lower Spec Limit (dB/cm) Upper Spec limit (dB/cm)
300 0 3.13
500 0 2.31
800 0 1.09

As for waveguide loss you can define no minimum loss (0 dB/cm) and you only define the maximum accepted loss (Upper Spec Limit)

Lets find a wafer pkey for this project, so that we can trigger the wafer analysis on it.

wafer_data = query.wafers().execute().data
wafer_pkeys = [wafer["pk"] for wafer in wafer_data]

from gdsfactoryhub.functions.wafer import aggregate_die_analyses

aggregate_die_analyses.run(
    wafer_pkey=wafer_pkeys[0],
    die_function_id="propagation-loss",
    output_key="propagation_loss",
    min_output=0.084,
    max_output=0.09,
)

with gfh.suppress_api_error():
    api.upload_function(
        function_id="aggregate_die_analyses",
        target_model="wafer",
        file=gfh.get_module_path(aggregate_die_analyses),
    )

task_ids = []
for wafer_pkey in tqdm(wafer_pkeys):
    task_id = api.start_analysis(
        analysis_id="wafer_propagation_loss",
        function_id="aggregate_die_analyses",
        target_model="wafer",
        target_model_pk=wafer_pkey,
        kwargs={
            "wafer_pkey": wafer_pkeys[0],
            "die_function_id": "propagation-loss",
            "output_key": "propagation_loss",
            "min_output": 0.084,
            "max_output": 0.09,
        },
    )
    task_ids.append(task_id)

result = api.wait_for_result(task_ids[0])
result.summary_plot()
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