Package: neural-ilt
Neural-ILTNeural-ILT is an end-to-end learning-based mask optimization tool developed by the research team supervised by Prof. Evangeline F.Y. Young in The Chinese University of Hong Kong (CUHK). Neural-ILT attempts to replace the conventional end-to-end ILT (inverse lithography technology) correction process under a holistic learning-based framework. It conducts on-neural-network ILT correction for the given layout under the guidiance of a partial coherent imaging model and directly outputs the optimized mask at the convergence.Compared to the conventional academia ILT solutions, e.g.,MOSAIC(Gaoet al., DAC'14) andGAN-OPC(Yanget al., TCAD'20), Neural-ILT enjoys:much faster ILT correction process (20x ~ 70x runtime speedup)better mask printability at convergencemodular design for easy customization and upgradation...More details are in the following papers:Jiang, Bentian, Lixin Liu, Yuzhe Ma, Hang Zhang, Bei Yu, and Evangeline FY Young. "Neural-ILT: migrating ILT to neural networks for mask printability and complexity co-optimization", in 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pp. 1-9. IEEE, 2020.Jiang, Bentian, Xiaopeng Zhang, Lixin Liu, and Evangeline FY Young. "Building up End-to-end Mask Optimization Framework with Self-training", in Proceedings of the 2021 International Symposium on Physical Design (ISPD), pp. 63-70. 2021.Jiang, Bentian, Lixin Liu, Yuzhe Ma, Bei Yu, and Evangeline FY Young. "Neural-ILT 2.0: Migrating ILT to Domain-specific and Multi-task-enabled Neural Network." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2021).Requirementspython:3.7.3pytorch:1.8.0torchvision:0.2.2cudatoolkit:11.1.1pillow:6.1.0GPU: >= 10GB GPU memory for pretrain, >= 7GB for Neural-ILTUsageStep 1:Download the source codes. For example,$gitclonehttps://github.com/cuhk-eda/neural-ilt.gitStep 2:Go to the project root and unzip the environment$cdNeural-ILT/
$unzipenv.zip(Optional) To replace the ICCAD'20 training dataset with the ISPD'21 training dataset (last batch)$cdNeural-ILT/dataset/
$unzipispd21_train_dataset.zipStep 3:Conduct Neural-ILT onICCAD 2013 mask optimization contest benchmarks$cdNeural-ILT/
$pythonneural_ilt.pyNote that we observed minor variation (±0.5%) on mask printability score (L2+PVB,statistics of 50 rounds). We haven't yet located the source of non-determinism. We would appreciate any insight from the community for resovling this non-determinism :sparkles:.Step 4 (optional):Backbone model pre-training$cdNeural-ILT/
$pythonpretrain_model.pyEvaluation:Evaluate the mask printability$cdNeural-ILT/
$pythoneval.py--layout_root[root_to_layout_file]--layout_file_name[your_layout_file_name]--mask_root[root_to_mask_file]--mask_file_name[your_mask_file_name]Parameters|── neural_ilt.py
 |   ├── device/gpu_no: the device id
 |   ├── load_model_name/ilt_model_path: the pre-trained model of Neural-ILT
 |   ├── lr: initial learning rate
 |   ├── refine_iter_num: maximum on-neural-network ILT correction iterations
 |   ├── beta: hyper-parameter for cplx_loss in the Neural-ILT objective
 |   ├── gamma: lr decay rate
 |   ├── step_size: lr decay step size
 |   ├── bbox_margin: the margin of the crop bbox
 |
 |── pretrain_model.py
 |   ├── gpu_no: the device id
 |   ├── num_epoch: number of training epochs
 |   ├── alpha: cycle loss weight for l2
 |   ├── beta: cycle loss weight for cplx
 |   ├── lr: initial learning rate
 |   ├── gamma: lr decay rate
 |   ├── step_size: lr decay step size
 |   ├── margin: the margin of the crop bbox
 |   ├── read_ref: read the pre-computed crop bbox for each layout
 |
 |── EndExpolre your own recipe for model pretraining and Neural-ILT. Have fun! :smile:AcknowledgementWe wolud like to thank the authors ofGAN-OPC(Yanget al., TCAD'20) for providing thetraining layoutsused in our ICCAD'20 paper. Based on which, we further generated the ISPD'21 training layouts following the procedure described inJianget al., ISPD'21.ContactBentian Jiang(btjiang@cse.cuhk.edu.hk) andLixin Liu(lxliu@cse.cuhk.edu.hk)CitationIf Neural-ILT is useful for your research, please consider citing the following papers:@inproceedings{jiang2020neural,
  title={Neural-ILT: migrating ILT to neural networks for mask printability and complexity co-optimization},
  author={Jiang, Bentian and Liu, Lixin and Ma, Yuzhe and Zhang, Hang and Yu, Bei and Young, Evangeline FY},
  booktitle={2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD)},
  pages={1--9},
  year={2020},
  organization={IEEE}
}
@inproceedings{jiang2021building,
  title={Building up End-to-end Mask Optimization Framework with Self-training},
  author={Jiang, Bentian and Zhang, Xiaopeng and Liu, Lixin and Young, Evangeline FY},
  booktitle={Proceedings of the 2021 International Symposium on Physical Design},
  pages={63--70},
  year={2021}
}
@article{jiang2021neural,
  title={Neural-ILT 2.0: Migrating ILT to Domain-specific and Multi-task-enabled Neural Network},
  author={Jiang, Bentian and Liu, Lixin and Ma, Yuzhe and Yu, Bei and Young, Evangeline FY},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2021},
  publisher={IEEE}
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