  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.
