metadata

language:
  - en
size_categories:
  - 50K<n<100K
license: mit
task_categories:
  - tabular-regression
tags:
  - photonics
  - silicon-nitride
  - waveguide
  - optical
  - dataset
  - synthetic
dataset_info:
  features:
    - name: waveguide_width
      dtype: float
    - name: waveguide_height
      dtype: float
    - name: cladding_material
      dtype: string
    - name: cladding_thickness
      dtype: float
    - name: deposition_method
      dtype: string
    - name: etch_method
      dtype: string
    - name: sidewall_roughness
      dtype: float
    - name: annealing_params
      dtype: string
    - name: wavelength
      dtype: float
    - name: polarization
      dtype: string
    - name: input_power
      dtype: float
    - name: temperature
      dtype: float
    - name: bend_radius
      dtype: float
    - name: device_length
      dtype: float
    - name: insertion_loss
      dtype: float
    - name: propagation_loss
      dtype: float
    - name: coupling_efficiency_input
      dtype: float
    - name: coupling_efficiency_output
      dtype: float
    - name: scattering_loss
      dtype: float
    - name: effective_index
      dtype: float
    - name: mode_confinement_factor
      dtype: float
    - name: batch_id
      dtype: string
    - name: data_source
      dtype: string
    - name: measurement_method
      dtype: string
    - name: uncertainty
      dtype: float
dataset_size: 90000
dataset_version: 1.0.0

SiN Photonic Waveguide Loss & Efficiency Dataset

Description
This dataset provides 90,000 synthetic rows of silicon nitride (Si₃N₄) photonic waveguide parameters, focusing on waveguide loss and efficiency metrics. The data is useful for modeling, simulation, or LLM fine tuning to predict and understand the relationship between fabrication/design parameters and optical performance.

Key Highlights ✨

Material Focus: Silicon Nitride (Si₃N₄)
Columns: 25 structured columns capturing waveguide geometry, fabrication method, operational conditions, and measured/synthetic performance metrics
Size: 90,000 rows (ideal for both training and validation splits)
Use Cases:
- Waveguide loss prediction
- Process control and optimization
- Photonic design parameter studies
- Synthetic data augmentation for AI/ML tasks

Dataset Structure 🏗️

Each row corresponds to a single waveguide configuration or measurement instance, including:

Waveguide Geometry
- waveguide_width (µm)
- waveguide_height (nm or µm)
- bend_radius (µm)
- device_length (mm)
Material & Fabrication
- cladding_material
- cladding_thickness (µm)
- deposition_method
- etch_method
- sidewall_roughness (nm)
- annealing_params
Operational Parameters
- wavelength (nm)
- polarization (TE/TM)
- input_power (dBm)
- temperature (°C)
Performance Metrics
- insertion_loss (dB)
- propagation_loss (dB/cm)
- coupling_efficiency_input (%)
- coupling_efficiency_output (%)
- scattering_loss (dB/cm)
- effective_index
- mode_confinement_factor (0–1)
Metadata
- batch_id (fabrication batch/wafer ID)
- data_source (Synthetic or Measurement)
- measurement_method (e.g., cut-back, ring_resonance)
- uncertainty (± dB or %)

Example Row

waveguide_width = 1.212
waveguide_height = 400.00
cladding_material = SiO2
cladding_thickness = 2.50
deposition_method = LPCVD
etch_method = RIE
sidewall_roughness = 2.05
annealing_params = 900C_3hr
wavelength = 1552.23
polarization = TE
input_power = 0.00
temperature = 25.00
bend_radius = 50.00
device_length = 10.00
insertion_loss = 3.50
propagation_loss = 0.300
coupling_efficiency_input = 72.00
coupling_efficiency_output = 68.00
scattering_loss = 0.15
effective_index = 1.800
mode_confinement_factor = 0.80
batch_id = BATCH_12
data_source = Synthetic
measurement_method = ring_resonance
uncertainty = 0.05

How to Use 💡

Download/Clone

You can download the CSV file manually or use Hugging Face’s datasets library:

from datasets import load_dataset

dataset = load_dataset("username/SiN_Photonic_Waveguide_Loss_Efficiency")

Loading & Exploration
- Load into your favorite Python environment (pandas, polars, etc.) to quickly explore the data distribution:
```
import pandas as pd

df = pd.read_csv("SiN_Photonic_Waveguide_Loss_Efficiency.csv")
print(df.head())
```

Model Training

For tasks like waveguide loss prediction, treat the waveguide geometry/fabrication columns as input features, and the insertion_loss or propagation_loss columns as the labels or targets.

Example ML scenario:

features = df[[
    "waveguide_width", "waveguide_height", "sidewall_roughness", 
    "wavelength", "polarization", "temperature"
]]
target = df["propagation_loss"]

# Then train a regression model, e.g., scikit-learn, XGBoost, etc.

Synthetic Data Augmentation
- Use this synthetic dataset to supplement smaller real datasets

Caveats & Limitations ⚠️

Synthetic Nature: While ranges are inspired by real-world photonic designs, actual values may differ based on specific foundries, tools, and processes.
Statistical Simplifications: Not all real-world correlations or distributions (e.g., non-uniform doping profiles, advanced thermal effects) are captured.
Measurement Noise: The uncertainty column does not fully replicate complex measurement artifacts.

License 📄

This dataset is available under the MIT License. You are free to modify, distribute, and use it for commercial or non-commercial purposes—just provide attribution.

Citation & Acknowledgments 🙌

If you use this dataset in your research or applications, please cite it as follows (example citation):

Author: https://huggingface.co/Taylor658
Title: SiN Photonic Waveguide Loss & Efficiency (Synthetic)
Year: 2025

@misc{sin_waveguide_loss_efficiency_2025,
  title  = {SiN Photonic Waveguide Loss & Efficiency (Synthetic)},
  author = {atayloraeropsace},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/username/SiN_Photonic_Waveguide_Loss_Efficiency}}
}

Contributing 🧑‍💻

We welcome community contributions, ideas, and corrections:

Add additional columns (e.g., doping profiles, stress levels, advanced measurement data).