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"]