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

TL;DR

FuNToM introduces a neural network-based functional modeling approach for RF circuits that reduces training data requirements and modeling time, enabling efficient synthesis across multiple topologies with high accuracy.

Contribution

The paper presents FuNToM, a novel functional modeling method leveraging two-port analysis and neural networks to model multiple RF circuit topologies with less data and time.

Findings

Reduces training data by 2.8x to 10.9x compared to state-of-the-art methods.

Requires 176.8x to 188.6x less time for training data collection.

Maintains comparable accuracy across different RF circuit topologies.

Abstract

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.