# FuNToM: Functional Modeling of RF Circuits Using a Neural Network Assisted Two-Port Analysis Method

arXiv: https://arxiv.org/abs/2308.02050 · HTML: https://mlsanity.com/paper/2308.02050/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 25 references that are in the map.
- First posted on arXiv 3 Aug 2023.

## Built on by (most cited first, 5 of 5)

- [MuaLLM: A Multimodal Large Language Model Agent for Circuit Design Assistance with Hybrid Contextual Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.08137/) (2025)
- [ZeroSim: Zero-Shot Analog Circuit Evaluation with Unified Transformer Embeddings](https://mlsanity.com/paper/2511.07658/) (2025)
- [ORACLE: A Multi-Objective Reinforcement Learning-Based Analog Circuit Design Optimizer with Large Language Models-Guided Exploration](https://mlsanity.com/paper/2608.04999/) (2026)
- [RF-Informed Graph Neural Networks for Accurate and Data-Efficient Circuit Performance Prediction](https://mlsanity.com/paper/2508.16403/) (2025)
- [TARGet: Topology-Aware Fusion-based Radio Frequency Circuit Functional Modeling using Graph Neural Networks](https://mlsanity.com/paper/2609.22165/) (2026)

## Abstract (arXiv)

Automatic synthesis of analog and Radio Frequency (RF) circuits is a trending approach that requires an efficient circuit modeling method. This is due to the expensive cost of running a large number of simulations at each synthesis cycle. Artificial intelligence methods are promising approaches for circuit modeling due to their speed and relative accuracy. However, existing approaches require a large amount of training data, which is still collected using simulation runs. In addition, such approaches collect a whole separate dataset for each circuit topology even if a single element is added or removed. These matters are only exacerbated by the need for post-layout modeling simulations, which take even longer. To alleviate these drawbacks, in this paper, we present FuNToM, a functional modeling method for RF circuits. FuNToM leverages the two-port analysis method for modeling multiple topologies using a single main dataset and multiple small datasets. It also leverages neural networks which have shown promising results in predicting the behavior of circuits. Our results show that for multiple RF circuits, in comparison to the state-of-the-art works, while maintaining the same accuracy, the required training data is reduced by 2.8x - 10.9x. In addition, FuNToM needs 176.8x - 188.6x less time for collecting the training set in post-layout modeling.