{"slug": "scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at", "title": "SCALE: Self-Supervised Constraint-Aware Layout GEneration for Local P&R DRV Fixing at Advanced Nodes", "summary": "Researchers propose SCALE, a self-supervised constraint-aware layout generation framework for local place-and-route design-rule violation fixing at sub-2nm nodes, boosting state-of-the-art agents' solve rates by +12–25% (up to 97%) on 100 real cases. The framework serializes multi-layer layout geometry into structured text, uses a fine-tuned language model to reconstruct masked polygons, and generates DRC-annotated layout-violation pairs to fine-tune a domain-adapted DRC-VLM.", "body_md": "arXiv:2607.21850v1 Announce Type: new\nAbstract: As semiconductor manufacturing advances toward sub-2nm nodes, local place-and-route (P&R) design-rule violation (DRV) fixing is increasingly limited by complex rule interactions, dense multi-layer routing geometries, and foundry-specific constraints. While Large Language Models (LLMs) have recently demonstrated strong capabilities in EDA scripting and documentation, their application to visual layout understanding remains largely unexplored: diagnosing DRC violations from layout imagery demands precise geometric reasoning and foundry-specific rule knowledge absent from general-purpose VLM training. We propose SCALE, a framework with a self-supervised layout-generation stage for local DRV fixing at advanced nodes. Multi-layer layout geometry is serialized into structured text, and a fine-tuned language model learns to reconstruct randomly masked polygons from surrounding BEOL context alone without violation labels. At inference, natural-language rule constraints and high-temperature sampling steer generation toward diverse, violation-prone layout variants validated by an industrial signoff DRC checker, producing DRC-annotated layout--violation pairs used to fine-tune a domain-adapted DRC-VLM. This VLM provides rule-aware geometric guidance for local DRV repair, boosting state-of-the-art agents' solve rates by +12--25% (up to 97%) on 100 real sub-2nm cases spanning enclosure, spacing, width, and color-spacing violations.", "url": "https://wpnews.pro/news/scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at", "canonical_source": "https://arxiv.org/abs/2607.21850", "published_at": "2026-07-27 04:00:00+00:00", "updated_at": "2026-07-27 04:27:37.758138+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "computer-vision", "ai-research"], "entities": ["SCALE", "arXiv", "DRC-VLM"], "alternates": {"html": "https://wpnews.pro/news/scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at", "markdown": "https://wpnews.pro/news/scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at.md", "text": "https://wpnews.pro/news/scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at.txt", "jsonld": "https://wpnews.pro/news/scale-self-supervised-constraint-aware-layout-generation-for-local-p-r-drv-at.jsonld"}}