---
language:
- en
license: mit
pipeline_tag: text-generation
tags:
- analog-circuits
- circuit-generation
- transformer
- generative-model
---

# AnalogToBi

AnalogToBi is a generative framework for device-level analog circuit topology generation, introduced in the paper [AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding](https://huggingface.co/papers/2603.08720). 

The model generates valid and novel analog circuit topologies conditioned on a target circuit type using a Transformer decoder.

## Key Features

- **Circuit-type conditioning**: Explicit functional control across 15 circuit categories (e.g., OpAmp, LDO, Comparator).
- **Bipartite graph representation**: Decouples devices and nets into distinct node types for better structural generalization.
- **Grammar-guided decoding**: State machine-based constrained decoding enforces electrical validity during generation.
- **Device renaming augmentation**: Randomizes device numbering to mitigate memorization and improve novelty.

Experimental results show that AnalogToBi achieves 97.8% validity and 92.1% novelty in generated circuits without human-in-the-loop training.

---

## Paper

[AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding](https://arxiv.org/abs/2603.08720)

---

## Code

Official implementation: [https://github.com/Seungmin0825/AnalogToBi](https://github.com/Seungmin0825/AnalogToBi)

---

## Usage

To generate circuit topologies using the grammar-guided decoder, you can use the following command from the official repository:

```bash
python GPT_Inference_Grammar.py CIRCUIT_Opamp
```

## Citation

```bibtex
@article{kim2026analogtobi,
  title={AnalogToBi: Device-Level Analog Circuit Topology Generation via Bipartite Graph and Grammar Guided Decoding},
  author={Kim, Seungmin and Kim, Mingun and Lee, Yuna and Kim, Yulhwa},
  journal={arXiv preprint arXiv:2603.08720},
  year={2026}
}
```