| --- |
| 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} |
| } |
| ``` |
