LithoHoD: A Litho Simulator-Powered Framework for IC Layout Hotspot Detection

TL;DR

This paper introduces LithoHoD, a novel framework combining lithography simulation and object detection to improve hotspot detection accuracy in advanced VLSI layouts, addressing generalization issues of existing methods.

Contribution

The framework integrates a lithography simulator with an object detection network using cross-attention, enabling more accurate and generalizable hotspot detection in IC layouts.

Findings

Outperforms previous state-of-the-art methods on real-world data

Effectively combines lithography simulation with object detection

Enhances generalization to real-world scenarios

Abstract

Recent advances in VLSI fabrication technology have led to die shrinkage and increased layout density, creating an urgent demand for advanced hotspot detection techniques. However, by taking an object detection network as the backbone, recent learning-based hotspot detectors learn to recognize only the problematic layout patterns in the training data. This fact makes these hotspot detectors difficult to generalize to real-world scenarios. We propose a novel lithography simulator-powered hotspot detection framework to overcome this difficulty. Our framework integrates a lithography simulator with an object detection backbone, merging the extracted latent features from both the simulator and the object detector via well-designed cross-attention blocks. Consequently, the proposed framework can be used to detect potential hotspot regions based on I) the variation of possible circuit shape deformation estimated by the lithography simulator, and ii) the problematic layout patterns already known. To this end, we utilize RetinaNet with a feature pyramid network as the object detection backbone and leverage LithoNet as the lithography simulator. Extensive experiments demonstrate that our proposed simulator-guided hotspot detection framework outperforms previous state-of-the-art methods on real-world data.