Presentation
LLM-HD: Layout Language Model for Hotspot Detection with GDS Semantic Encoding
DescriptionWith the rapid downscaling of technology nodes,
industrial flow such as pitch reduction, patterning flexibility, and lithography processing variability have been challenged.
Layout hotspot detection is one of the most challenging and critical steps, which requires technology upgrading.
Pattern matching and learning-based detectors are proposed as quick detection methods.
However, these computer vision (CV) model-based detectors use images transformed from layout GDS files as their inputs.
It leads to foreground information (e.g. metal polygons) loss and even distortion when shrinking the image size to fit the model input.
Moreover, plenty of irrelevant background information such as non-polygon pixels are also fed into the model,
which hinders the fitting of the model and results in a waste of computational resources.
Concerning the disadvantage of the traditional CV model, we propose a new layout hotspot detection paradigm,
which directly detects hotspots on GDS files by exploiting a hierarchical GDS semantic representation scheme and a well-designed pre-trained natural language processing (NLP) model.
Compared with state-of-the-art works,
ours achieves better results both on the ICCAD2012 metal layer benchmark and the more challenging ICCAD2020 via layer benchmark, which demonstrates the effectiveness and efficiency of our approach.
industrial flow such as pitch reduction, patterning flexibility, and lithography processing variability have been challenged.
Layout hotspot detection is one of the most challenging and critical steps, which requires technology upgrading.
Pattern matching and learning-based detectors are proposed as quick detection methods.
However, these computer vision (CV) model-based detectors use images transformed from layout GDS files as their inputs.
It leads to foreground information (e.g. metal polygons) loss and even distortion when shrinking the image size to fit the model input.
Moreover, plenty of irrelevant background information such as non-polygon pixels are also fed into the model,
which hinders the fitting of the model and results in a waste of computational resources.
Concerning the disadvantage of the traditional CV model, we propose a new layout hotspot detection paradigm,
which directly detects hotspots on GDS files by exploiting a hierarchical GDS semantic representation scheme and a well-designed pre-trained natural language processing (NLP) model.
Compared with state-of-the-art works,
ours achieves better results both on the ICCAD2012 metal layer benchmark and the more challenging ICCAD2020 via layer benchmark, which demonstrates the effectiveness and efficiency of our approach.
Event Type
Research Manuscript
TimeWednesday, June 262:00pm - 2:15pm PDT
Location3004, 3rd Floor
Design
Design for Manufacturability and Reliability