Advances in artificial neural network models of active devices

This paper reviews some recent advances in the application of artificial neural networks (ANNs) to measurement-based modeling of active devices. For transistor models, the advent of the adjoint training method for terminal charges, and the training of constitutive relations depending on multiple dynamical variables - some identified from measured waveform data from nonlinear measurements - are surveyed. The ability to implement exact discrete symmetry constraints in ANN-based models is another example. Several examples of practical models implemented in commercial simulation tools are cited to demonstrate that ANN technology has become a mainstream tool for advanced measurement-based modeling of active devices. Areas for future development are also outlined.