Correlation-Aware Mask Optimization with Modulated Reinforcement Learning for Improved Optical Proximity Correction in VLSI Manufacturing

Core Concepts

CAMO, a reinforcement learning-based optical proximity correction (OPC) framework, explicitly integrates important principles of the OPC problem, including spatial correlation among neighboring segments and an OPC-inspired modulation for movement action selection, to outperform state-of-the-art OPC techniques.

Abstract

The article presents CAMO, a reinforcement learning (RL)-based optical proximity correction (OPC) framework that addresses the limitations of existing ML-based OPC approaches.
Key highlights:

Existing ML-based OPC techniques, such as regression-based and generative models, are typically data-driven and lack consideration of OPC-specific principles, leading to potential performance or efficiency bottlenecks.
CAMO explicitly incorporates the spatial correlation among neighboring segments and an OPC-inspired modulation for movement action selection to improve mask quality.
The spatial correlation is captured by encoding the layout into a graph, using a graph neural network (GNN) to fuse node features along the graph edges, and employing a recurrent neural network (RNN) to sequentially analyze the node embeddings and coordinate segment movements.
An OPC-inspired modulation module is proposed to guide the RL training and boost the optimization, addressing the efficiency and generalization issues.
Experiments on via layer patterns and metal layer patterns demonstrate that CAMO outperforms state-of-the-art OPC techniques from both academia and industry.

Stats

The article does not provide any specific numerical data or metrics to support the key logics. The performance improvements of CAMO are described qualitatively.

Quotes

The article does not contain any striking quotes that support the key logics.

Key Insights Distilled From

CAMO

by Xiaoxiao Lia... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00980.pdf

Deeper Inquiries

How can the spatial correlation among segments be further exploited to improve the mask optimization beyond the current GNN and RNN-based approach

To further enhance mask optimization by exploiting spatial correlation among segments, additional techniques can be incorporated into the CAMO framework. One approach could involve introducing a more advanced graph neural network (GNN) architecture that can capture higher-order dependencies among segments. By utilizing graph convolutional networks (GCNs) or graph attention networks (GATs), the model can better understand the relationships between segments beyond immediate neighbors. This would enable the model to consider global layout information and long-range dependencies, leading to more accurate predictions and improved mask quality.
Another strategy to leverage spatial correlation is to integrate reinforcement learning (RL) algorithms that focus on multi-agent coordination. By treating each segment as an agent in a collaborative environment, the model can learn to coordinate movements among neighboring segments more effectively. Techniques like multi-agent deep reinforcement learning or cooperative RL can be explored to encourage segments to work together towards a common goal, resulting in optimized mask designs with enhanced spatial correlation awareness.
Furthermore, incorporating techniques from graph theory, such as graph alignment algorithms or graph similarity measures, can help identify and exploit structural similarities among segments. By aligning segments with similar spatial characteristics, the model can learn from patterns that exhibit consistent spatial correlations, leading to more robust and efficient mask optimization.

What are the potential limitations of the OPC-inspired modulation module, and how can it be further improved to enhance the training efficiency and generalization

The OPC-inspired modulation module in the CAMO framework may face limitations in terms of adaptability to diverse layout patterns and manufacturing processes. One potential limitation is the fixed modulation scheme, which may not be optimal for all types of designs or lithography processes. To address this limitation, the modulation module can be enhanced by introducing adaptive modulation strategies that dynamically adjust the modulation parameters based on the complexity and characteristics of the layout patterns.
Another limitation could be the reliance on domain-specific knowledge for modulation, which may not always generalize well to new or unseen patterns. To overcome this, the modulation module can be augmented with meta-learning techniques that enable the model to learn and adapt its modulation strategy across different OPC scenarios. By training the modulation module on a diverse set of layouts and lithography processes, the model can improve its ability to generalize and optimize masks efficiently.
Additionally, the modulation module's training efficiency can be further improved by incorporating techniques like curriculum learning or transfer learning. By gradually increasing the complexity of the training data or leveraging knowledge from pre-trained models, the module can learn more effectively and generalize better to new OPC tasks.

How can the CAMO framework be extended to handle other types of layout patterns or manufacturing processes beyond optical lithography

To extend the CAMO framework to handle other types of layout patterns or manufacturing processes beyond optical lithography, several modifications and enhancements can be considered. One approach is to adapt the input encoding and feature extraction methods to accommodate different types of patterns, such as electron beam lithography or nanoimprint lithography. By customizing the squish pattern encoding or introducing new encoding techniques tailored to specific manufacturing processes, the model can effectively analyze and optimize masks for diverse lithography techniques.
Furthermore, the CAMO framework can be extended to support multi-layer optimization by incorporating inter-layer dependencies and interactions. By integrating a hierarchical RL approach that considers interactions between different layers and their impact on mask quality, the model can optimize masks across multiple layers simultaneously, leading to more efficient and accurate results.
Moreover, the CAMO framework can be expanded to handle non-standard lithography processes, such as extreme ultraviolet lithography (EUV) or directed self-assembly (DSA). By incorporating domain-specific knowledge and data from these processes, the model can adapt its optimization strategies to address the unique challenges and requirements of each manufacturing technique, enabling robust and effective mask optimization for a wide range of lithography processes.