README.md

metadata

license: cc-by-4.0
task_categories:
  - image-to-image
language:
  - en
pretty_name: CellOPC
size_categories:
  - 100K<n<1M
tags:
  - optical-proximity-correction
  - inverse-lithography
  - mask-optimization
  - vlsi
  - eda
  - lithography
dataset_info:
  features:
    - name: image
      dtype: image
    - name: conditioning_image
      dtype: image
    - name: mask_type
      dtype: string
    - name: context
      dtype: int32
    - name: source_dataset
      dtype: string
  splits:
    - name: train
      num_bytes: 2488911027
      num_examples: 451912
    - name: validation
      num_bytes: 626298112
      num_examples: 112974
    - name: test
      num_bytes: 621060
      num_examples: 80
  download_size: 4285592645
  dataset_size: 3115830199
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

CellOPC

CellOPC is a large-scale benchmark dataset for cell- and context-aware mask optimization. It is designed to support deep learning research for optical proximity correction (OPC) and inverse lithography technique (ILT) mask generation.

Dataset Description

CellOPC is constructed from real integrated circuit layouts at the 45 nm technology node. Each sample is clipped around a standard-cell placement instance to preserve cell-level hierarchy and surrounding layout context. The dataset provides paired input layout/target images and optimized mask images for learning cell-wise mask generation under different context sizes.

The dataset contains both model-based OPC and ILT mask types. It is intended to evaluate how standard-cell identity, neighboring geometries, and input context size affect mask prediction and lithography-aware printability.

Dataset Structure

Each sample contains the following fields:

• conditioning_image: input target/layout image.
• image: ground-truth optimized mask image.
• mask_type: mask generation type, such as opc or ilt.
• context: context size used when clipping the input layout.
• source_dataset: source subset name, such as cellopc_opc_16.

The dataset contains three splits:

Split	Number of Examples
train	451,912
validation	112,974
test	80

Intended Use

CellOPC is intended for:

• training image-to-image mask generation models;
• benchmarking deep learning methods for OPC and ILT;
• studying the impact of context size on mask prediction;
• evaluating cell-aware and context-aware mask optimization.

Loading the Dataset

from datasets import load_dataset

dataset = load_dataset("ChristyHu/CellOPC")

train_set = dataset["train"]
val_set = dataset["validation"]
test_set = dataset["test"]

sample = train_set[0]
layout = sample["conditioning_image"]
mask = sample["image"]
mask_type = sample["mask_type"]
context = sample["context"]