AnalogCoder: Analog Circuit Design via Training-Free Code Generation

New: Our paper is accepted by AAAI 2025 as an Oral Presentation (Top 5%), and selected as AAAI 2025 Most Influential Papers.

This repository contains the official implementation of the AAAI 2025 paper:

AnalogCoder: Analog Circuit Design via Training-Free Code Generation

Authors:
Yao Lai¹, Sungyoung Lee², Guojin Chen³, Souradip Poddar², Mengkang Hu¹, David Z. Pan², Ping Luo¹

¹ The University of Hong Kong
² The University of Texas at Austin
³ The Chinese University of Hong Kong

   

🏆 Highlights

• AAAI 2025 Oral Presentation (Top 5%)
• AAAI 2025 Top-15 Influential Papers [Rank 11]

Links: [Paper]

NEW RELEASE
An extended version, AnalogCoder-Pro, is available:
[Paper] [Repo]

Introduction

Analog circuit design is a critical component of modern chip development. It involves selecting device types, defining connectivity, and tuning parameters to meet functional and performance requirements. While LLMs have shown strong capabilities in digital design tasks, analog design remains challenging due to the complexity of circuits and the scarcity of high-quality data.

To address these challenges, we introduce AnalogCoder, the first training-free LLM agent for analog circuit design that formulates design as Python code generation.

Key advantages

• Feedback-enhanced agentic workflow with domain-specific prompts for robust autonomous design.
• A circuit skill library that archives successful designs as reusable modular subcircuits, enabling easier composition of complex systems.
• Extensive evaluation on a curated benchmark of 24 analog circuit design tasks shows that AnalogCoder successfully designed 20 circuits, outperforming existing baselines.

In summary, AnalogCoder helps reduce the labor intensity of analog chip design and enables non-experts to design analog circuits more efficiently.

Evaluation of LLMs

Ranking rule: higher #Solved (successfully solved circuit design tasks) ranks first. If tied, higher Avg. Pass@1 ranks first.

LLM Model	Avg. Pass@1	Avg. Pass@5	#Solved
Llama2-7B	0.0	0.0	0
Llama2-13B	0.0	0.0	0
SemiKong-8B*	0.1	0.7	1
Llama3-8B	0.1	0.7	1
Phi3-14B	0.3	1.3	1
Qwen-1.5-110B	0.3	1.4	2
CodeLlama-13B	0.6	2.5	2
Mistral-7B	3.3	7.7	2
Llama 2-70B	5.1	9.8	3
CodeQwen-1.5-7B	1.1	5.6	4
CodeLlama-34B	1.9	7.4	4
CodeLlama-7B	2.4	9.0	4
DeepSeek-Coder-33B	4.0	10.2	4
Llama3.1-8B	4.9	12.9	4
Magicoder-7B	3.8	8.6	5
Mixtral-8×7B	5.6	12.4	5
StarCoder2-15B-Instruct	5.6	12.4	5
CodeGeeX4-9B*	10.6	20.3	6
CodeLlama-70B	3.2	12.2	7
CodeGemma-7B	6.9	17.0	7
WizardCoder-33B	7.1	16.9	7
GPT-3.5 (w/o context)	8.1	18.5	7
GPT-3.5 (w/o flow)	12.8	25.3	8
Codestral-22B	16.4	29.1	8
GPT-3.5 (w/o CoT)	19.4	26.3	8
GLM-4	22.8	31.2	8
GPT-3.5 (SPICE)	13.9	26.9	9
GPT-3.5	21.4	35.0	10
GPT-3.5 (fine-tune)	28.1	39.6	10
Llama3-70B	28.8	36.4	11
Gemini-1.0-Pro	28.9	41.2	11
Gemini-1.5-Flash	35.7	40.6	11
Qwen-2-72B	9.3	26.6	12
GPT-4o-mini	34.9	41.7	12
DeepSeek-V2-Chat	38.6	44.3	13
GPT-4 (w/o tool)	51.1	57.7	14
Llama3.1-70B	25.4	42.6	14
GPT-4o (w/o tool)	54.2	58.9	15
Claude-3.5-Sonnet (w/o tool)	58.1	60.7	15
Mistral-Large-2	28.6	51.0	17
Gemini-1.5-Pro	33.9	44.6	17
DeepSeek-V2-Coder	56.5	69.2	19
Llama3.1-405B	56.9	70.7	20
AnalogCoder (GPT-4o-based)	66.1	75.9	20
AnalogCoder (Claude-3.5-based)	76.1	86.3	22

* Without CoT (prompting the model to directly generate code rather than explicit device-level reasoning) due to token limitations or model specialization.

Notes

1. All results are reproducible.
2. The core Python environment setup does not require sudo privileges.
   (However, installing system dependencies such as ngspice or ollama may require admin permissions depending on your OS.)

Installation

AnalogCoder requires:

• Python ≥ 3.10
• PySpice ≥ 1.5
• openai ≥ 1.16.1
• ngspice (as the SPICE backend)

Option A: Install via environment.yml (recommended)

git clone https://github.com/laiyao1/AnalogCoder.git
cd AnalogCoder
conda env create -f environment.yml
conda activate analog

Option B: Manual installation

git clone https://github.com/laiyao1/AnalogCoder.git
cd AnalogCoder

conda create -n analog python=3.11 -y
conda activate analog

pip install matplotlib pandas numpy scipy ollama openai

# SPICE backend (recommended via conda-forge)
conda install -c conda-forge ngspice -y

# PySpice
git clone https://github.com/PySpice-org/PySpice.git
cd PySpice
pip install -e .
cd ..

(Optional) Ollama Setup (for local open-source LLMs)

This section is only needed if you want to run open-source LLMs locally.

1. Install Ollama (Linux)

sudo apt-get update
sudo apt-get install -y zstd
curl -fsSL https://ollama.com/install.sh | sh

2. Start the Ollama server

nohup ollama serve > ollama.log 2>&1 &

3. Pull a model (example: qwen3-coder)

ollama pull qwen3-coder

Environment Check

To verify your environment:

cd sample_design
python test_all_sample_design.py

If the output contains All tasks passed, your environment is ready.

If it prints Please check your environment and try again, please re-check your Python environment—especially PySpice and ngspice related setup.

Quick Start

Run with OpenAI models

python gpt_run.py --task_id=1 --api_key="[OPENAI_API_KEY]" --num_per_task=1

This generates one circuit solution for task 1.

Run with a local open-source model (Ollama)

Make sure ollama serve is running first, then:

python gpt_run.py --model qwen3-coder --task_id=1

Benchmark

• Task descriptions: problem_set.tsv
• Sample circuits: sample_design/
• Testbenches / checking scripts: problem_check/

Citation

If you find this work useful, please cite:

@article{lai2025analogcoder,
    title={AnalogCoder: Analog Circuit Design via Training-Free Code Generation},
    volume={39},
    DOI={10.1609/aaai.v39i1.32016},
    number={1},
    journal={Proceedings of the AAAI Conference on Artificial Intelligence},
    author={Lai, Yao and Lee, Sungyoung and Chen, Guojin and Poddar, Souradip and Hu, Mengkang and Pan, David Z. and Luo, Ping},
    year={2025},
    pages={379-387}
}

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