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.

翻译：模拟与射频（RF）电路的自动综合是一种新兴方法，亟需高效的电路建模技术。这是因为在每个综合周期中运行大量仿真会产生高昂成本。人工智能方法因其快速性和相对准确性成为电路建模的重要途径。然而，现有方法需要大量训练数据，而这些数据仍通过仿真运行获取。此外，即使仅增减单个元件，这类方法也需要为每种电路拓扑收集完整独立的数据集。这些问题在需要耗时更长的后布局建模仿真中更加突出。为缓解上述缺陷，本文提出FuNToM——一种面向射频电路的功能建模方法。FuNToM利用双端口分析方法，通过单一主数据集和多个小数据集实现多种拓扑的建模，并采用在电路行为预测中已展现出良好效果的神经网络。实验结果表明，与现有最优方法相比，在保持相同精度的情况下，多种射频电路的训练数据需求量减少了2.8倍至10.9倍。此外，在后布局建模中，FuNToM的训练集收集时间可缩短176.8倍至188.6倍。