| --- |
| license: mit |
| task_categories: |
| - image-to-image |
| tags: |
| - lithography |
| - physics-simulation |
| - semiconductor |
| - chemistry |
| size_categories: |
| - n<1K |
| --- |
| |
| # vFab-1.0-Photolithography-ML-surrogate-Tensor-Dataset |
|
|
| This dataset contains generated physics simulations for semiconductor photolithography, formatted as Native NCHW `.npy` arrays for direct ingestion into PyTorch models. |
|
|
| ## Dataset Structure |
|
|
| The data is split into two simulation phases: |
|
|
| **1. Aerial Image Dataset (`aerial_image_dataset/`)** |
| * **Inputs (`X_input/`):** Shape `[4, 512, 512]`. Contains: |
| * Channel 0: Binary Layout Mask (Vertical/Horizontal Gratings, Contact Arrays, etc.) |
| * Channel 1: Normalized Dose (mJ) |
| * Channel 2: Normalized Defocus (nm) |
| * Channel 3: Normalized Numerical Aperture (NA) |
| * **Targets (`Y_normalized_aerial/`):** Shape `[1, 512, 512]`. Hopkins-Abbe optical intensity simulation. |
| |
| **2. Post-Exposure Bake (PEB) Dataset (`peb_dataset/`)** |
| * **Targets (`Y_target_M/`):** Shape `[1, 512, 512]`. Chemical reaction matrices (M-matrix) simulating the acid-base diffusion and polymer deprotection during the PEB step. |
|
|
| ## Usage |
| Data can be loaded directly using NumPy and PyTorch: |
| ```python |
| import numpy as np |
| import torch |
| |
| x_input = np.load('X_input/sample_0.npy') |
| tensor_x = torch.tensor(x_input, dtype=torch.float32) |
| |
| ``` |
|
|
| > **Note:** The Hugging Face Dataset Viewer will show an error for this repository because it cannot natively render `.npy` tensor arrays in the browser. The dataset is fully functional and can be downloaded using the `huggingface_hub` library. |
