OmniLayout: A Schematic-Coupled Multimodal Benchmark for Constraint-Aware Geometric Reasoning in PCB Layout

2026-03-05 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Engineering & Applied Sciences · Depth: Expert, long

Summary

OmniLayout is introduced as the first benchmark designed to evaluate large language models (LLMs) on printed-circuit-board (PCB) layout placement, addressing a critical gap in reasoning under strict spatial and functional constraints within electronic design automation (EDA) workflows. The benchmark comprises 1,681 industrial-grade schematic-coupled PCB layouts, featuring 77.24K IC placement instances constrained within board boundaries. It includes four core tasks: geometric reasoning for IC physical placement, routability-aware placement, electrical functionality preservation, and tool-augmented agentic reasoning. Initial evaluations of models like Claude-Sonnet-4.6 and GPT-5.5 reveal substantial limitations in current LMMs regarding weak geometric reasoning, poor routability optimization, and inconsistent preservation of electrical functionality in complex PCB designs.

Key takeaway

For machine learning engineers developing LMMs for electronic design automation, this benchmark highlights critical weaknesses in spatial and constraint-aware reasoning for PCB layout. You should prioritize improving LMMs' ability to handle dense object placement, ensure geometric legality, optimize routability, and preserve electrical functionality, potentially through iterative, tool-augmented agentic approaches. This is crucial for practical application in complex industrial PCB design workflows.

Key insights

OmniLayout benchmarks LMMs on PCB layout, revealing their current limitations in constraint-aware geometric and functional placement.

Principles

• PCB placement is a non-convex NP-hard problem.
• Real-world PCB designs are almost exclusively proprietary.
• LMMs struggle with dense, constrained spatial reasoning.

Method

OmniLayout uses an automated pipeline to curate 1,681 real-world PCB layouts with schematics and reference designs, evaluating geometric legality, routability, electrical functionality, and agentic tool use.

In practice

• Evaluate LMMs using the OmniLayout benchmark.
• Develop LMMs for tool-augmented PCB placement.

Topics

• PCB Layout
• Large Multimodal Models
• Electronic Design Automation
• Geometric Reasoning
• Constraint Satisfaction
• Benchmarks

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.