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PCB-Bench: Benchmarking LLMs for Printed Circuit Board Placement and Routing

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLMsPrinted Circuit BoardPlacement and RoutingMultimodal Benchmark
Abstract:

Recent advances in Large Language Models (LLMs) have enabled impressive capabilities across diverse reasoning and generation tasks. However, their ability to understand and operate on real-world engineering problems—such as Printed Circuit Board (PCB) placement and routing—remains underexplored due to the lack of standardized benchmarks and high-fidelity datasets. To address this gap, we introduce PCB-Bench, the first comprehensive benchmark designed to systematically evaluate LLMs in the context of PCB design. PCB-Bench spans three complementary task settings: (1) text-based reasoning with approximately 3,700 expert-annotated instances, consisting of over 1,800 question-answer pairs and their corresponding choice question versions, covering component placement, routing strategies, and design rule compliance; (2) multimodal image-text reasoning with approximately 500 problems requiring joint interpretation of PCB visuals and technical specifications, including component identification, function recognition, and visual trace reasoning; (3) real-world design comprehension using over 170 complete PCB projects with schematics, placement files, and design documentation. We design structured evaluation protocols to assess both generative and discriminative capabilities, and conduct extensive comparisons across state-of-the-art LLMs. Our results reveal substantial gaps in current models’ ability to reason over spatial placements, follow domain-specific constraints, and interpret professional engineering artifacts. PCB-Bench establishes a foundational resource for advancing research toward more capable engineering AI, with implications extending beyond PCB design to broader structured reasoning domains. Data and code are available at https://anonymous.4open.science/r/ICLR_submission_PCB-Bench-CDC5.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces PCB-Bench, a comprehensive benchmark spanning text-based reasoning, multimodal image-text tasks, and real-world design comprehension for evaluating LLMs on PCB design. According to the taxonomy tree, it occupies the 'Comprehensive Multi-Task PCB Benchmarks' leaf under 'Benchmark Development and Evaluation Frameworks'. Notably, this leaf contains only the original paper itself with no sibling papers, indicating this is a relatively sparse research direction. The broader parent branch includes one other leaf focused on IC physical design benchmarks, suggesting limited prior work specifically targeting multi-task PCB evaluation.

The taxonomy reveals two main branches: benchmark development and application-oriented methods. The application branch contains multiple active subtopics including direct LLM routing assistance, generative transformer routing, LLM-guided optimization, placement methods, and general circuit design tools. These neighboring directions emphasize practical deployment rather than systematic evaluation. The scope notes clarify that benchmark work excludes application-focused methods, while application methods exclude benchmark creation, establishing clear boundaries. This structural separation suggests the paper addresses a distinct gap in standardized evaluation infrastructure that complements existing application-oriented research.

Among thirty candidates examined across three contributions, none yielded refutable prior work. The first contribution, PCB-Bench as a comprehensive multimodal benchmark, examined ten candidates with zero refutations. Similarly, the high-quality dataset contribution and systematic evaluation protocols each examined ten candidates without finding overlapping prior work. This pattern across all contributions suggests that within the limited search scope, no existing work provides comparable multi-task PCB benchmarking infrastructure combining text reasoning, multimodal understanding, and real-world design comprehension at this scale.

Based on the limited top-thirty semantic search, the work appears to occupy a novel position in PCB design evaluation. The absence of sibling papers in its taxonomy leaf and zero refutations across contributions indicate limited direct precedent. However, this assessment reflects the examined candidate pool rather than exhaustive coverage of all PCB benchmarking efforts. The taxonomy structure suggests the paper bridges a gap between application-focused methods and standardized evaluation frameworks.

Taxonomy

Core-task Taxonomy Papers
6
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: evaluating large language models on printed circuit board placement and routing tasks. The field structure divides into two main branches. The first, Benchmark Development and Evaluation Frameworks, focuses on creating standardized testbeds and metrics to assess how well LLMs handle PCB design challenges, ranging from component placement to trace routing. The second branch, Application-Oriented LLM Methods, emphasizes practical techniques that adapt or fine-tune models for real-world PCB workflows, often integrating domain-specific heuristics or optimization strategies. Representative works such as Strengthening IC Foundations[1] and Routing GPT[4] illustrate how researchers build specialized datasets and evaluation protocols, while others like PCBAgent[3] and LLM Power PCB Optimization[5] demonstrate end-to-end systems that leverage LLMs for automated design tasks. Several active lines of work highlight contrasting emphases and open questions. Some studies prioritize comprehensive multi-task benchmarks that test a broad spectrum of PCB operations, whereas others concentrate on narrower subtasks like routing or placement optimization. A key trade-off emerges between generality—developing benchmarks that cover diverse board complexities—and depth in capturing nuanced design constraints. PCB-Bench[0] sits squarely within the comprehensive multi-task benchmark cluster, aiming to provide a holistic evaluation suite that spans placement and routing challenges. Compared to more application-focused efforts such as LLMs for PCB Routing[2] or AI Circuit Builder[6], which target specific deployment scenarios, PCB-Bench[0] emphasizes rigorous, standardized assessment across multiple task dimensions, helping the community understand where current LLMs excel and where they still struggle in PCB design.

Claimed Contributions

PCB-Bench: A Comprehensive Multimodal Benchmark for PCB Design

The authors propose PCB-Bench, the first benchmark for evaluating large language models on printed circuit board placement and routing tasks. It spans three complementary settings: text-based reasoning with approximately 3,700 expert-annotated instances, multimodal image-text reasoning with approximately 500 problems, and real-world design comprehension using over 170 complete PCB projects.

10 retrieved papers
High-Quality Dataset of Real-World PCB Designs

The authors collect and release over 170 complete PCB designs from OSHWHub, each including schematic diagrams, placement files, design documentation, and representative screenshots. This dataset serves as a resource for future supervised training and pretraining on realistic EDA artifacts.

10 retrieved papers
Systematic Evaluation Protocols and Model Assessment

The authors establish standardized evaluation protocols with unified task formats, metrics (BERTScore, SBERT, accuracy), and prompt design procedures. They systematically evaluate state-of-the-art models across multiple tasks and modalities, revealing substantial gaps in current models' ability to reason over spatial placements and follow domain-specific constraints.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

PCB-Bench: A Comprehensive Multimodal Benchmark for PCB Design

The authors propose PCB-Bench, the first benchmark for evaluating large language models on printed circuit board placement and routing tasks. It spans three complementary settings: text-based reasoning with approximately 3,700 expert-annotated instances, multimodal image-text reasoning with approximately 500 problems, and real-world design comprehension using over 170 complete PCB projects.

Contribution

High-Quality Dataset of Real-World PCB Designs

The authors collect and release over 170 complete PCB designs from OSHWHub, each including schematic diagrams, placement files, design documentation, and representative screenshots. This dataset serves as a resource for future supervised training and pretraining on realistic EDA artifacts.

Contribution

Systematic Evaluation Protocols and Model Assessment

The authors establish standardized evaluation protocols with unified task formats, metrics (BERTScore, SBERT, accuracy), and prompt design procedures. They systematically evaluate state-of-the-art models across multiple tasks and modalities, revealing substantial gaps in current models' ability to reason over spatial placements and follow domain-specific constraints.

Table of Contents