# CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning

arXiv: https://arxiv.org/abs/2404.00980 · HTML: https://mlsanity.com/paper/2404.00980/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 3 of 3)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [DAMO: Deep Agile Mask Optimization for Full Chip Scale](https://mlsanity.com/paper/2008.00806/) (2020)
- [From IC Layout to Die Photo: A CNN-Based Data-Driven Approach](https://mlsanity.com/paper/2002.04967/) (2020)

## Built on by (most cited first, 2 of 2)

- [SemiKong: Curating, Training, and Evaluating A Semiconductor Industry-Specific Large Language Model](https://mlsanity.com/paper/2411.13802/) (2024)
- [Intelligent OPC Engineer Assistant for Semiconductor Manufacturing](https://mlsanity.com/paper/2408.12775/) (2024)

## Abstract (arXiv)

Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing. Various OPC approaches based on machine learning have been proposed to pursue performance and efficiency, which are typically data-driven and hardly involve any particular considerations of the OPC problem, leading to potential performance or efficiency bottlenecks. In this paper, we propose CAMO, a reinforcement learning-based OPC system that specifically integrates important principles of the OPC problem. CAMO explicitly involves the spatial correlation among the movements of neighboring segments and an OPC-inspired modulation for movement action selection. Experiments are conducted on both via layer patterns and metal layer patterns. The results demonstrate that CAMO outperforms state-of-the-art OPC engines from both academia and industry.