Late Breaking Results: Encoder-Decoder Generative Diffusion Transformer Towards Push-Button Analog IC Sizing

In this paper, disruptive research using generative diffusion models (DMs) with an attention-based encoder-decoder backbone is conducted to automate the sizing of analog integrated circuits (ICs). Unlike time-consuming optimization-based methods, the encoder-decoder DM is able to sample accurate solutions at push-button speed by solving the inverse sizing problem. Experimental results show that the proposed model outperforms the most recent deep learningbased techniques, presenting higher generalization capabilities to performance targets not seen during training.