# ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddings

arXiv: https://arxiv.org/abs/2511.07658 · HTML: https://mlsanity.com/paper/2511.07658/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 37 references that are in the map.
- First posted on arXiv 10 Nov 2025.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [DeeperGCN: All You Need to Train Deeper GCNs](https://mlsanity.com/paper/2006.07739/) (2020)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [AnalogCoder: Analog Circuit Design via Training-Free Code Generation](https://mlsanity.com/paper/2405.14918/) (2024)
- [CktGNN: Circuit Graph Neural Network for Electronic Design Automation](https://mlsanity.com/paper/2308.16406/) (2023)
- [AutoCkt: Deep Reinforcement Learning of Analog Circuit Designs](https://mlsanity.com/paper/2001.01808/) (2020)
- [AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies](https://mlsanity.com/paper/2503.00205/) (2025)
- [LaMAGIC: Language-Model-based Topology Generation for Analog Integrated Circuits](https://mlsanity.com/paper/2407.18269/) (2024)
- [Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design](https://mlsanity.com/paper/2203.15913/) (2022)
- [AICircuit: A Multi-Level Dataset and Benchmark for AI-Driven Analog Integrated Circuit Design](https://mlsanity.com/paper/2407.18272/) (2024)
- [RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning](https://mlsanity.com/paper/2407.19150/) (2024)
- [AnalogGym: An Open and Practical Testing Suite for Analog Circuit Synthesis](https://mlsanity.com/paper/2409.08534/) (2024)
- [ALIGN: A System for Automating Analog Layout](https://mlsanity.com/paper/2008.10682/) (2020)
- [INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers](https://mlsanity.com/paper/2407.07346/) (2024)
- [FuNToM: Functional Modeling of RF Circuits Using a Neural Network Assisted Two-Port Analysis Method](https://mlsanity.com/paper/2308.02050/) (2023)

## Built on by (most cited first, 2 of 2)

- [AnalogSAGE: Self-evolving Analog Design Multi-Agents with Stratified Memory and Grounded Experience](https://mlsanity.com/paper/2512.22435/) (2025)
- [PowerGenie: Analytically-Guided Evolutionary Discovery of Superior Reconfigurable Power Converters](https://mlsanity.com/paper/2601.21984/) (2026)

## Abstract (arXiv)

Although recent advancements in learning-based analog circuit design automation have tackled tasks such as topology generation, device sizing, and layout synthesis, efficient performance evaluation remains a major bottleneck. Traditional SPICE simulations are time-consuming, while existing machine learning methods often require topology-specific retraining or manual substructure segmentation for fine-tuning, hindering scalability and adaptability. In this work, we propose ZeroSim, a transformer-based performance modeling framework designed to achieve robust in-distribution generalization across trained topologies under novel parameter configurations and zero-shot generalization to unseen topologies without any fine-tuning. We apply three key enabling strategies: (1) a diverse training corpus of 3.6 million instances covering over 60 amplifier topologies, (2) unified topology embeddings leveraging global-aware tokens and hierarchical attention to robustly generalize to novel circuits, and (3) a topology-conditioned parameter mapping approach that maintains consistent structural representations independent of parameter variations. Our experimental results demonstrate that ZeroSim significantly outperforms baseline models such as multilayer perceptrons, graph neural networks and transformers, delivering accurate zero-shot predictions across different amplifier topologies. Additionally, when integrated into a reinforcement learning-based parameter optimization pipeline, ZeroSim achieves a remarkable speedup (13x) compared to conventional SPICE simulations, underscoring its practical value for a wide range of analog circuit design automation tasks.