# ICCAD2019Benchmarks

**Repository Path**: abraham-xu/ICCAD2019Benchmarks

## Basic Information

- **Project Name**: ICCAD2019Benchmarks
- **Description**: https://github.com/gauravr1991/ICCAD2019Benchmarks
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-08-26
- **Last Updated**: 2025-08-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# ICCAD-2019 Benchmarks
## Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders 
Link to the paper: [https://ieeexplore.ieee.org/document/8942128]

The Datasets and the source code mentioned in the paper are shared in this repository.

### Technical details of the proposed ICCAD-2019 benchmarks
 - Layer Numbers
	  - 0: Extent
	  - 10: Metal polygons
	  - 21: Hotspot core marker
	  - 23: Non-Hotspot core marker
 - Hotspots and Non-Hotspots can be identified in one of the following ways:
	  - Through their corresponding marker layers.
	  - Cellnames: Every pattern in the file has a unique name in the form of a cellname. Hotspot patterns contain the keyword `_hotspot` in their cell names, whereas, Non-Hotspot patterns contain `_nonhotspot`.
	  - Labels: Every pattern contains a text label at its center (in layer 0). The label is same as the cell name.
 - Identifying Truly-Never-Seen-Before (TNSB) hotspots within the Testing Dataset - 1:
	 - A CSV file containing the cell names of TNSB hotspots is included in the same folder as the Testing Dataset - 1. 
### Simple ML-based hotspot detection flow (discussed in section 3 of the paper)
- The source code (Jupyter notebook (Python 2.7)), training and testing datasets, and the pre-trained models are made available.
- Users can either use the pre-trained models or re-train them locally. Instructions to switch between the two modes, to change dataset paths etc., are provided in the main code.
### Source code of State-Of-The-Art (SOTA) methods
- Source code of `DAC'17 [12]` and `TCAD'18 [11]` can be found in [link](https://github.com/phdyang007/dlhsd)
- Source code of `SMACD'18 [13]` can be found in [link](https://github.com/unnir/lithography_hotspot_detection)
- We have not publicly released the source code of `VTS'18 [26]` yet. Therefore, to obtain the source code of `VTS'18 [26]`, please contact us directly.

### Citation
```
@inproceedings{reddy2019machine,
  title={Machine Learning-Based Hotspot Detection: Fallacies, Pitfalls and Marching Orders},
  author={Reddy, Gaurav Rajavendra and Madkour, Kareem and Makris, Yiorgos},
  booktitle={2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)},
  pages={1--8},
  year={2019},
  organization={IEEE}
}
```