In this paper, a two-stage artificial intelligence framework for supporting the signal integrity compliant printed circuit board design process has been developed. This framework was applied to compare linear and non-linear data, which showed that the non-linear data source gains advantages over the linear data in terms of attainable regression accuracy. For the investigated application however, the linear model can be utilized directly or by utilizing transfer learning for adequate results.

The implementation of an anomaly detection with a decision tree was investigated with respect to the evaluation of selected SI effects in PCB design. The tree based approach allows the designer to understand the proposals more easily (explainable AI). High prediction accuracies for two simple networks were achieved with the proposed method. The use of anomaly detection with a decision tree will be further developed in future work for various more complex SI applications in circuit design.

The effects of parameters (decaps, power plane and stackup) affecting the input impedance of a power delivery network are investigated. An artificial neural network is trained using the generated data utilizing a process to generate suitable input for training a machine learning module, which is able to predict the impedance profile of the PDN. In order to obtain a more accurate prediction, Bayesian optimization is implemented and the results are compared to commercial power integrity software.

This paper presents a guided wave Six-Port interferometric radar system with integrated I/Q offset cancelation. An intelligent algorithm is proposed which optimally adjusts three voltage controlled attenuators and one voltage controlled phase shifter in the radio frequency part of the system. By minimizing the I/Q offset, the standard deviation as well as the relative error are decreased in comparison to an uncompensated measurement setup.

Further improvements on the novel method for state of charge (SOC) determination of lithium iron phosphate (LFP) cells based on the impedance spectra classification are presented.