### Install Project Dependencies

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Installs all required Python packages for the TorchResist project from the 'requirements.txt' file. Ensure this file is in the current directory before running.

```bash
pip3 install -r requirements.txt
```

--------------------------------

### Generate FuILT Lithography Results

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Use this command to generate lithography results with the FuILT model. Masks can have an optional 1nm resolution controlled by a parameter.

```bash
python3 -m examples.fuilt --mask ./data/MetalSet/mask/1nm/images --resolution 1.0 --outpath ./data/MetalSet/fuilt/1nm/litho
```

--------------------------------

### Run Resist Simulation

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Execute the resist simulation script. Specify the lithography model, path to lithography results, output directory, and resolution.

```bash
python3 -m examples.resist --lithomodel FUILT --lithoresult ./data/MetalSet/fuilt/1nm/litho/numpys --outpath ./data/MetalSet/fuilt/1nm/resist --resolution 1.0
```

--------------------------------

### Navigate to Project Directory

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Changes the current working directory to the root of the cloned TorchResist project. This is necessary to run subsequent commands within the project context.

```bash
cd TorchResist
```

--------------------------------

### Clone TorchResist Repository

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Clones the TorchResist project repository from GitHub. This command downloads the entire project source code to your local machine.

```bash
git clone https://github.com/ShiningSord/TorchResist.git
```

--------------------------------

### Generate ICCAD13 Lithography Results

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Use this script to generate lithography results with the ICCAD13 model. Ensure masks are in the specified directory and a configuration file is provided.

```bash
python3 -m examples.iccad13 --mask ./data/MetalSet/mask/1nm/images --outpath ./data/MetalSet/iccad13/1nm/litho --config ./simulator/lithobench/config/lithosimple
```

--------------------------------

### Create and Activate Conda Environment

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Creates a new Anaconda environment named 'torchresist' with Python 3.10 and activates it. This is the first step for setting up the project environment.

```bash
conda create -n torchresist python==3.10
conda activate torchresist
```

--------------------------------

### Downsample Masks to 7nm Resolution

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Executes a Python script to downsample mask images to 7nm resolution. This is an optional step to enhance efficiency for certain mask types.

```bash
python3 tools/downsampling.py
```

--------------------------------

### Process Mask Data

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Automates the process of organizing downloaded litho mask data into the required directory structure for TorchResist. Replace 'path/to/lithodata.tar.gz' with the actual file path.

```bash
bash scripts/processmask.sh path/to/lithodata.tar.gz
```

--------------------------------

### TorchResist Citation

Source: https://github.com/shiningsord/torchresist/blob/main/README.md

Cite this paper when using TorchResist in your research.

```bibtex
@inproceedings{wang2025torchresist,
  title={TorchResist: open-source differentiable resist simulator},
  author={Wang, Zixiao and Zhou, Jieya and Zheng, Su and Yin, Shuo and Liang, Kaichao and Hu, Shoubo and Chen, Xiao and Yu, Bei},
  booktitle={DTCO and Computational Patterning IV},
  volume={13425},
  pages={397--405},
  year={2025},
  organization={SPIE}
}
```

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