We review the recent advances on the implementation of electronic circuits that operate in the millimeter-wave (30–300 GHz) and terahertz (300–3000 GHz) frequency ranges. The focus of this article is on nonlinear phenomena in electronics. The different implementations of nonlinear circuits for the sake of millimeter-wave and terahertz signal generation are studied in this paper. The challenges of signal generation are examined and the benefits and limitations of different schemes of signal generation are discussed. It is shown that nonlinear devices such as electronic transistors exhibit major advantages enabling realization of low-cost and portable circuits for the emerging applications in these frequency ranges. We also review linear and nonlinear design methodologies employing the properties of electromagnetic waves. The electronic systems designed based on the presented ideas are shown to push the previously unbeatable limits of operation in millimeter-wave and terahertz frequency ranges. A discussion on remaining challenges and future directions concludes the paper.

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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.

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