Comparisons

Wafer and die comparisons

You can compare any dies or wafers, as another type of aggregated analysis

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

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

import gdsfactoryhub as gfh

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

wafers = query.wafers().execute().data
wafer_ids = [w["wafer_id"] for w in wafers]

die_analyses_per_wafer = {}

for wafer_id in wafer_ids:
    die_analyses_per_wafer[wafer_id] = (
        query.analyses()
        .not_.is_("die", "null")  # only keep die analyses
        .eq("die.wafer.wafer_id", wafer_id)  # only keep die analyses for this wafer
        .eq("function.function_id", "die_iv_sheet_resistance")  # only keep die analyses for this function
        .execute()
        .data
    )

df = pd.DataFrame(
    [
        {
            "die_pkey": analysis["die"]["pk"],
            "sheet_resistance": analysis["output"].get("sheet_resistance", np.nan),
            "wafer_id": wafer_id,
        }
        for wafer_id, analyses in die_analyses_per_wafer.items()
        for analysis in analyses
    ]
)
df.head()
die_pkey sheet_resistance wafer_id
0 c2f2c95f-20c6-447d-938e-02179ce4d5d4 NaN 6d4c615ff105
1 5af3d39f-cdaa-4c54-b3f1-08e274b089b6 NaN 6d4c615ff105
2 1898f9f5-bfef-4528-90f8-04c6abf41aa1 8.293516e+01 6d4c615ff105
3 91fc175a-2204-41b4-b528-1899ade4872a -1.055094e+12 6d4c615ff105
4 3a12038a-b7ed-4b23-a4d1-ed3a163857b9 8.926339e+01 6d4c615ff105 
def remove_outliers(df, column_name="sheet_resistance"):
    Q1 = df[column_name].quantile(0.25)
    Q3 = df[column_name].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    return df[(df[column_name] >= lower_bound) & (df[column_name] <= upper_bound)]


filtered_df = remove_outliers(df)

filtered_df.head()
die_pkey sheet_resistance wafer_id
2 1898f9f5-bfef-4528-90f8-04c6abf41aa1 82.935164 6d4c615ff105
4 3a12038a-b7ed-4b23-a4d1-ed3a163857b9 89.263391 6d4c615ff105
5 594affdb-f3f1-4604-a097-50255a8893d0 88.175527 6d4c615ff105
6 5d71ad5c-1c76-4c46-9d6a-cb6a199b8191 92.697775 6d4c615ff105
7 3debdd16-762d-47eb-80e2-db02534fcb4f 88.342298 6d4c615ff105 
df.boxplot(column="sheet_resistance", by="wafer_id", grid=False)
plt.title("before removing outliers")
plt.suptitle("")
plt.xlabel("Wafer ID")
plt.ylabel("Sheet Resistance")
plt.grid(True, which="major", axis="y", color="gray", alpha=0.5, linestyle="--")
plt.gca().xaxis.grid(True, color="gray", alpha=0.5, linestyle="--")
plt.tight_layout()
plt.show()

png

filtered_df.boxplot(column="sheet_resistance", by="wafer_id", grid=False)
plt.title("after removing outliers")
plt.suptitle("")
plt.xlabel("Wafer ID")
plt.ylabel("Sheet Resistance")
plt.grid(True, which="major", axis="y", color="gray", alpha=0.5, linestyle="--")
plt.gca().xaxis.grid(True, color="gray", alpha=0.5, linestyle="--")
plt.tight_layout()
plt.show()

png

Wafer Analysis Generate Layout
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