Update README.md

README.md

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----
-license: cc-by-4.0
----
+---
+language: 
+  - en
+size_categories:
+  - 50K<n<100K
+license: mit
+task_categories:
+  - tabular-prediction
+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:
+
+1. **Waveguide Geometry**  
+   - `waveguide_width` (µm)  
+   - `waveguide_height` (nm or µm)  
+   - `bend_radius` (µm)  
+   - `device_length` (mm)
+
+2. **Material & Fabrication**  
+   - `cladding_material`  
+   - `cladding_thickness` (µm)  
+   - `deposition_method`  
+   - `etch_method`  
+   - `sidewall_roughness` (nm)  
+   - `annealing_params`
+
+3. **Operational Parameters**  
+   - `wavelength` (nm)  
+   - `polarization` (TE/TM)  
+   - `input_power` (dBm)  
+   - `temperature` (°C)
+
+4. **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)
+
+5. **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 💡
+1. **Download/Clone**  
+   - You can download the CSV file manually or use Hugging Face’s `datasets` library:  
+     ```python
+     from datasets import load_dataset
+
+     dataset = load_dataset("username/SiN_Photonic_Waveguide_Loss_Efficiency")
+     ```
+
+2. **Loading & Exploration**  
+   - Load into your favorite Python environment (`pandas`, `polars`, etc.) to quickly explore the data distribution:
+     ```python
+     import pandas as pd
+
+     df = pd.read_csv("SiN_Photonic_Waveguide_Loss_Efficiency.csv")
+     print(df.head())
+     ```
+
+3. **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:  
+     ```python
+     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.
+     ```
+
+4. **Synthetic Data Augmentation**  
+   - Use this synthetic dataset to **supplement** smaller real datasets, enabling data-hungry deep learning models to generalize better.
+
+## Dataset Creation Process 🛠️
+A Python script was used to randomly generate each column’s values within plausible ranges based on typical Si₃N₄ waveguide fabrication and performance data. The insertion loss is partially derived from the propagation loss and device length, and additional random offsets account for coupling losses and measurement variability.
+
+## 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
+
+```bibtex
+@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).  
+
+
+---