Generate measurements

For this tutorial you will generate some sample (fake) measurement data so you can post it to your project.

You’re going to create a new folder and populate it with JSON files containing the fake measurement data for the whole wafer.

import json
import pandas as pd
import numpy as np
from pathlib import Path
import matplotlib.pyplot as plt

df = pd.read_csv("design_manifest.csv")
df

	cell	x	y	width_um	length_um	analysis	analysis_parameters
0	cutback_rib_assembled_MFalse_W0p3_L0	20150	60150	0.3	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
1	cutback_rib_assembled_MTrue_W0p3_L25000	1039250	60150	0.3	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
2	cutback_rib_assembled_MFalse_W0p3_L5000	20150	204150	0.3	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
3	cutback_rib_assembled_MTrue_W0p3_L20000	1039250	204150	0.3	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
4	cutback_rib_assembled_MFalse_W0p3_L10000	20150	348150	0.3	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
5	cutback_rib_assembled_MTrue_W0p3_L15000	1039250	348150	0.3	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
6	cutback_rib_assembled_MFalse_W0p5_L0	20250	492250	0.5	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
7	cutback_rib_assembled_MTrue_W0p5_L25000	1058750	492250	0.5	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
8	cutback_rib_assembled_MFalse_W0p5_L5000	20250	646250	0.5	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
9	cutback_rib_assembled_MTrue_W0p5_L20000	1058750	646250	0.5	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
10	cutback_rib_assembled_MFalse_W0p5_L10000	20250	800250	0.5	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
11	cutback_rib_assembled_MTrue_W0p5_L15000	1058750	800250	0.5	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
12	cutback_rib_assembled_MFalse_W0p8_L0	20400	954400	0.8	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
13	cutback_rib_assembled_MTrue_W0p8_L25000	1088000	954400	0.8	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
14	cutback_rib_assembled_MFalse_W0p8_L5000	20400	1123400	0.8	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
15	cutback_rib_assembled_MTrue_W0p8_L20000	1088000	1123400	0.8	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
16	cutback_rib_assembled_MFalse_W0p8_L10000	20400	1292400	0.8	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
17	cutback_rib_assembled_MTrue_W0p8_L15000	1088000	1292400	0.8	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
18	cutback_ridge_assembled_MFalse_W0p3_L0	20150	60150	0.3	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
19	cutback_ridge_assembled_MTrue_W0p3_L25000	1037250	60150	0.3	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
20	cutback_ridge_assembled_MFalse_W0p3_L5000	20150	203150	0.3	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
21	cutback_ridge_assembled_MTrue_W0p3_L20000	1037250	203150	0.3	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
22	cutback_ridge_assembled_MFalse_W0p3_L10000	20150	346150	0.3	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
23	cutback_ridge_assembled_MTrue_W0p3_L15000	1037250	346150	0.3	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
24	cutback_ridge_assembled_MFalse_W0p5_L0	20250	489250	0.5	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
25	cutback_ridge_assembled_MTrue_W0p5_L25000	1056750	489250	0.5	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
26	cutback_ridge_assembled_MFalse_W0p5_L5000	20250	642250	0.5	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
27	cutback_ridge_assembled_MTrue_W0p5_L20000	1056750	642250	0.5	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
28	cutback_ridge_assembled_MFalse_W0p5_L10000	20250	795250	0.5	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
29	cutback_ridge_assembled_MTrue_W0p5_L15000	1056750	795250	0.5	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
30	cutback_ridge_assembled_MFalse_W0p8_L0	20400	948400	0.8	0	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
31	cutback_ridge_assembled_MTrue_W0p8_L25000	1086000	948400	0.8	25000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
32	cutback_ridge_assembled_MFalse_W0p8_L5000	20400	1116400	0.8	5000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
33	cutback_ridge_assembled_MTrue_W0p8_L20000	1086000	1116400	0.8	20000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
34	cutback_ridge_assembled_MFalse_W0p8_L10000	20400	1284400	0.8	10000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]
35	cutback_ridge_assembled_MTrue_W0p8_L15000	1086000	1284400	0.8	15000	[power_envelope]	[{"n": 10, "wvl_of_interest_nm": 1550}]

def gaussian_grating_coupler_response(
    peak_power: float, center_wavelength: float, bandwidth_1dB, wavelength
):
    """Calculate the response of a Gaussian grating coupler.

    Args:
        peak_power: The peak power of the response.
        center_wavelength: The center wavelength of the grating coupler.
        bandwidth_1dB: The 1 dB bandwidth of the coupler.
        wavelength: The wavelength at which the response is evaluated.

    Returns:
    - The power of the grating coupler response at the given wavelength.
    """
    # Convert 1 dB bandwidth to standard deviation (sigma)
    sigma = bandwidth_1dB / (2 * np.sqrt(2 * np.log(10)))

    # Gaussian response calculation
    response = peak_power * np.exp(
        -0.5 * ((wavelength - center_wavelength) / sigma) ** 2
    )
    return response


nm = 1e-3
# Parameters
peak_power = 1.0
center_wavelength = 1550 * nm  # Center wavelength in micrometers
bandwidth_1dB = 100 * nm

# Wavelength range: 100 nm around the center wavelength, converted to micrometers
wavelength_range = np.linspace(center_wavelength - 0.05, center_wavelength + 0.05, 121)

# Calculate the response for each wavelength
responses = [
    gaussian_grating_coupler_response(peak_power, center_wavelength, bandwidth_1dB, wl)
    for wl in wavelength_range
]

# Plotting
plt.figure(figsize=(10, 6))
plt.plot(wavelength_range, responses)
plt.title("Gaussian Grating Coupler Response")
plt.xlabel("Wavelength (micrometers)")
plt.ylabel("Response")
plt.grid(True)
plt.show()

widths_um = [0.3, 0.5, 0.8]
loss_dB = [3, 2.5, 1]
loss_vs_width_model = np.polyfit(widths_um, loss_dB, deg=1)
plt.plot(widths_um, loss_dB, ".")
plt.xlabel("width (um)")
plt.ylabel("loss (dB/cm)")

Text(0, 0.5, 'loss (dB/cm)')

Generate wafer definitions

You can define different wafer maps for each wafer.

wafer_definitions = Path("wafer_definitions.json")
wafers = ["6d4c615ff105"]
dies = [
    {"x": x, "y": y}
    for y in range(-2, 3)
    for x in range(-2, 3)
    if not (abs(y) == 2 and abs(x) == 2)
]

# Wrap in a list with the wafer information
data = [
    {
        "wafer": wafer_pkey,
        "dies": dies,
        "lot_id": "lot1",
        "attributes": {"doping_n": 1e-18},
        "description": "low doping",
    }
    for wafer_pkey in wafers
]

with open(wafer_definitions, "w") as f:
    json.dump(data, f, indent=2)

Generate and write spectrums

You can easily generate some spectrum data and add some noise to make it look like a real measurement.

cwd = Path(".")
metadata = {"measurement_type": "Spectral MEAS", "temperature": 25}
noise_peak_to_peak_dB = 1
grating_coupler_loss_dB = 3

for wafer in wafers:
    wafer_per_cent_variation = 0.20  # 20% variation
    wafer_variation_factor = 1 + wafer_per_cent_variation * (2 * np.random.rand() - 1)

    for die in dies:
        die = f"{(die['x'])}_{(die['y'])}"
        for (_, row), (_, device_row) in zip(df.iterrows(), df.iterrows()):
            cell_id = row["cell"]
            if "ridge" in cell_id:
                continue
            top_cell_id = "RibLoss" if "rib" in cell_id else "RidgeLoss"
            device_id = f"{top_cell_id}_{cell_id}_{device_row['x']}_{device_row['y']}"
            dirpath = cwd / wafer / die / device_id
            dirpath.mkdir(exist_ok=True, parents=True)
            data_file = dirpath / "data.json"
            metadata_file = dirpath / "attributes.json"
            metadata_file.write_text(json.dumps(metadata))
            loss_dB = (
                np.polyval(loss_vs_width_model, row["width_um"])
                * row["length_um"]
                * 1e-6
                * 1e2
            )  # convert um to cm
            loss_dB += 2 * grating_coupler_loss_dB
            peak_power = 10 ** (-loss_dB / 10)
            device_per_cent_variation = 0.05  # 5% variation
            device_variation_factor = 1 + device_per_cent_variation * (
                2 * np.random.rand() - 1
            )
            peak_power *= wafer_variation_factor * device_variation_factor

            output_power = [
                gaussian_grating_coupler_response(
                    peak_power, center_wavelength, bandwidth_1dB, wl
                )
                for wl in wavelength_range
            ]
            output_power *= 10 ** (
                noise_peak_to_peak_dB * np.random.rand(len(wavelength_range)) / 10
            )
            # output_power_dB = 10*np.log10(output_power)
            d = {
                "wavelength": wavelength_range * 1e3,
                "output_power": output_power,
                "polyfit": loss_dB,
            }
            data = pd.DataFrame(d)
            json_converted_file = data.reset_index(drop=True).to_dict(orient="split")
            json.dump(
                json_converted_file,
                open(data_file.with_suffix(".json"), "w+"),
                indent=2,
            )

dirpath

PosixPath('6d4c615ff105/1_2/RibLoss_cutback_rib_assembled_MTrue_W0p8_L15000_1088000_1292400')

plt.plot(wavelength_range, output_power)
plt.title(dirpath.stem)
plt.ylabel("Power (mW)")
plt.xlabel("wavelength (um)")

Text(0.5, 0, 'wavelength (um)')

f"{len(list(dirpath.parent.glob('*/*.json')))//2} measurements"

'18 measurements'

dirpath

PosixPath('6d4c615ff105/1_2/RibLoss_cutback_rib_assembled_MTrue_W0p8_L15000_1088000_1292400')

f"{len(list(dirpath.parent.parent.glob('*')))} dies"

'21 dies'