Wafer Analysis

Wafer analysis

Now we will create wafer maps of the using the die analysis we triggered in the die 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.notebook import tqdm

import gdsfactoryhub as gfh

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

Wafer analysis

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

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

from gdsfactoryhub.functions.wafer import aggregate_die_analyses

wafer_pks = [w["pk"] for w in query.wafers().execute().data]
output = aggregate_die_analyses.run(
    wafer_pkey=wafer_pks[0],
    die_function_id="cutback_loss",
    output_key="component_loss",
    min_output=0.0,
    max_output=0.115,
)

png

with gfh.suppress_api_error():
    result = api.upload_function(
        function_id="aggregate_die_analyses",
        target_model="wafer",
        file=gfh.get_module_path(aggregate_die_analyses),
        test_target_model_pk=wafer_pks[0],
        test_kwargs={
            "die_function_id": "cutback_loss",
            "output_key": "component_loss",
            "min_output": 0.0,
            "max_output": 0.115,
        },
    )

Duplicate function

Run analysis on all wafers:

results = []
for wafer_pk in (pb := tqdm(wafer_pks)):
    pb.set_postfix(wafer_pk=wafer_pk)
    result = api.start_analysis(
        analysis_id=f"wafer_iv_sheet_resistance_{wafer_pk}",
        function_id="aggregate_die_analyses",
        target_model="wafer",
        target_model_pk=wafer_pk,
        kwargs={
            "die_function_id": "cutback_loss",
            "output_key": "component_loss",
            "min_output": 0.0,
            "max_output": 0.115,
        },
    )
    results.append(result)

  0%|          | 0/1 [00:00<?, ?it/s]

Let's have a look at the last analysis:

analysis_pks = [r["pk"] for r in results]
utils.analyses().wait_for_completion(pks=analysis_pks)
analyses = query.analyses().in_("pk", analysis_pks).execute().data
succesful_analyses = [a for a in analyses if a["status"] == "COMPLETED"]
analysis = succesful_analyses[-1]
img = api.download_plot(analysis["summary_plot"]["path"])
img.resize((530, 400))

Waiting for analyses:   0%|          | 0/1 [00:00<?, ?it/s]

png

Die Analysis Full Reference
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