__     _    __     __             ____                          __  
   / /    (_)  / /_   / /_   ____    / __ )  ___    ____   _____   / /_ 
  / /    / /  / __/  / __ \ / __ \  / __  | / _ \  / __ \ / ___/  / __ \
 / /___ / /  / /_   / / / // /_/ / / /_/ / /  __/ / / / // /__   / / / /
/_____//_/   \__/  /_/ /_/ \____/ /_____/  \___/ /_/ /_/ \___/  /_/ /_/ 
                                                                        

LithoBench: Benchmarking AI Computational Lithography for Semiconductor Manufacturing

Installation

Install Basic Dependencies

If you manage your python environments with anaconda, you can create a new environment with

conda create -n lithobench python=3.8
conda activate lithobench

To install the dependencies with pip, you can use

pip3 install -r requirements_pip.txt

You may install the dependencies with conda:

conda install --file requirements_conda.txt -c pytorch -c conda-forge

However, due to the complex environment solving, the process may be slow and the installed packages may be unsatisfactory. For example, you may get a CPU version of pytorch. Thus, if you want to use conda, you may install a GPU version of pytorch before you install other dependencies.

Note that we develop LithoBench with python 3.8 and pytorch 1.10. We also tested LithoBench with pytorch 2.0. The system we use is Ubuntu 18 with Intel Xeon CPUs and NVIDIA GPUs. We also tested the program on CentOS 7.

Install adaptive-boxes

The python package adaptive-boxes is needed for shot counting. You can install the package in the thirdparty/adaptive-boxes folder.

cd thirdparty/adaptive-boxes
pip3 install -e .

Run ILT algorithms

You can test the ILT method in LithoBench with the following commands:

CurvMulti

CUDA_VISIBLE_DEVICES=0 python3 pyilt/curvmulti.py