EEBench: Benchmarking AI's Ability to Design Circuit Boards

AI can now solve specific circuit design problems, but full-system autonomy remains distant

AI models are increasingly capable of designing functional electronic circuits, though they currently excel more at component selection and schematic logic than autonomous physical layout. While frontier models can now solve complex engineering trade-offs—such as maintaining voltage rails during power outages—they still require human oversight for physical routing and final hardware verification.

EEBench: A Simulation-Based Evaluation Framework

Measuring the quality of AI-generated electronics is challenging because graphical CAD tools are difficult for agents to navigate. EEBench solves this by using atopile, a declarative code-based circuit representation. This allows AI agents to work directly with components, connections, and electrical constraints rather than clicking through a GUI.

How Grading Works

EEBench uses a fully deterministic grading system based on SPICE simulations. Instead of judging if a schematic "looks plausible," the benchmark:

1. Builds the submitted design and constructs the circuit graph and bill of materials (BOM).
2. Runs SPICE simulations to measure specific electrical behaviors (e.g., gain, ripple, transient response).
3. Tests tolerance corners, rebuilding the SPICE deck to ensure the circuit works even when components deviate from nominal values.
4. Evaluates cost efficiency against a reference BOM, though cost is only considered after the circuit is proven functional.

Real-World Engineering Constraints

EEBench tests the AI's ability to handle "messy" real-world physics. For example, in a residential energy meter task, the AI must keep a processor alive for 20ms after power loss. A successful model must account for the fact that ceramic capacitors lose capacitance as voltage increases and must choose real manufacturer parts that are available and reasonably priced, rather than ideal textbook values.

Model Performance and Leaderboard Results

Recent results indicate a significant leap in engineering capabilities among frontier models. As of September 2026, the top performers on the EEBench V1 leaderboard include:

Model	Score
GPT-6 Astra	69.3%
Claude Opus 5	61.6%
Grok 4.6	57.1%
Claude Fable 5.1	56.4%
Gemini 3.8 Flash	55.4%

Notably, xAI has integrated EEBench into the Grok 4.6 model card under "engineering acceleration," suggesting that frontier labs are now using these benchmarks to quantify a model's ability to assist in hardware development.

Practical Applications and User Insights

Community feedback from experienced PCB designers highlights a shift from using AI for simple suggestions to using it for active collaboration and verification.

Successful Use Cases

• Rapid Prototyping: Users report using Claude and GPT models to design functional circuits (e.g., LED earrings, VGA monochrome image generators) with only minor manual corrections required.
• Review and Debugging: AI is being used to review PDF schematics and netlists to spot errors that humans missed, such as incorrect pin assignments or missing decoupling capacitors.
• Scripted Design: Some developers are using LLMs to write deterministic Python scripts (via Skidl) to generate KiCad files, reducing the risk of manual entry errors.

Current Limitations

Despite the progress in schematics, physical PCB layout remains a major bottleneck. Users note that:

• Routing is still a challenge: While AI can suggest component placement, autonomous routing often fails or requires significant human intervention.
• Lack of Physical Feedback: Unlike software, hardware cannot be "iterated" instantly. A hallucinated connection can lead to destroyed components (e.g., melted USB-C ports) and expensive fabrication cycles.
• Implicit Knowledge: Experienced designers argue that AI still lacks the implicit "tribal knowledge" of hardware engineering, such as managing ground loops or complex RF tricks, without explicit prompting or data sheets.

The Path Toward RL for Electronics

Because EEBench provides a deterministic reward signal (pass/fail based on simulation), it serves as a foundation for Reinforcement Learning (RL). Failed runs provide precise data on which voltage missed its limit or which operating corner failed, allowing labs to train models specifically for electronics engineering. This loop—design, simulate, grade, and refine—is expected to accelerate the transition from "textbook knowledge" to "engineering intuition."