{"type":"ScholarlyArticle","title":"Physics-Guided Geometric Diffusion for Macro Placement Generation","authors":[{"name":"Jongho Yoon","slug":"jongho-yoon","given":"Jongho","family":"Yoon","affiliation":""},{"name":"Jinsung Jeon","slug":"jinsung-jeon","given":"Jinsung","family":"Jeon","affiliation":""},{"name":"Seokhyeong Kang","slug":"seokhyeong-kang","given":"Seokhyeong","family":"Kang","affiliation":""}],"abstract":"Macro placement is a pivotal stage in VLSI physical design, fundamentally determining the overall chip performance. Recent data-driven placement methods have demonstrated significant potential, yet they often struggle to handle sequential dependencies and to balance topological connectivity with physical constraints. To bridge this gap, we propose MacroDiff+, a physics-guided geometric diffusion framework. Specifically, we design a dual-domain denoising architecture that couples topological connectivity encoded by heterogeneous GNNs with global geometric context modeled by a Transformer. Furthermore, we introduce Physics-Guided Sampling, an inference strategy that actively steers the generation using explicit gradients to ensure both statistical plausibility and physical validity. On the ISPD2005 MMS benchmarks, MacroDiff+ outperforms state-of-the-art baselines with a 6.1–6.2% reduction in wirelength. Notably, it exhibits superior stability and scalability on large-scale designs where prior methods fail to converge. The source code is provided at https://github.com/jhy00n/MacroDiff-plus.","doi":"10.24963/ijcai.2026/735","url":"https://ijcai.org/proceedings/2026/735","pdf":"https://ijcai.org/proceedings/2026/0735.pdf","markdown":"https://ijcai.org/proceedings/2026/735.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/735","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":735,"pages":{"first":6609,"last":6616},"keywords":["Domain-specific AI4Tech: AI4Manufacturing","Emerging AI4Tech : Emerging AI4Tech areas","AI4Tech infrastructure/systems: AI chips, AI sensors, AI computers","Advanced AI4Tech: Generative and LLMs-driven AI4Tech"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}