metadata
license: mit
language:
- en
tags:
- analog-circuits
- electronic-design-automation
- conditional-generation
- contrastive-learning
- vae
- diffusion
- test time optimization
library_name: pytorch
pipeline_tag: other
This repository hosts the pre-trained model weights for CktGen and baseline models.
📚 For full documentation, installation, training, and evaluation instructions, please visit our GitHub repository.
📁 Available Models
| Model | Description |
|---|---|
| CktGen | Specification-Conditioned Circuit Generator |
| Evaluator | Performance predictor (surrogate) |
| LDT | Latent Diffusion Transformer |
| CktGNN | Graph Neural Network |
| CVAEGAN | Conditional VAE-GAN |
| PACE | Parallel Convolution Encoder |
🚀 Usage
For download and usage instructions, please see our GitHub repository.
📊 Directory Structure
├── cktgen/ # Main CktGen models
│ ├── cktgen_cond_gen_*.pth # Conditional generation
│ └── cktgen_recon_*.pth # Reconstruction
├── evaluator/ # Performance predictor
│ └── evaluator_*.pth
└── baselines/ # Baseline models
├── cktgnn/
├── ldt/
├── pace/
└── cvaegan/
📖 Citation
@article{hou2025cktgen,
title = {CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence},
journal = {Engineering},
year = {2025},
doi = {https://doi.org/10.1016/j.eng.2025.12.025},
author = {Yuxuan Hou and Hehe Fan and Jianrong Zhang and Yue Zhang and Hua Chen and Min Zhou and Faxin Yu and Roger Zimmermann and Yi Yang},
}