Neural Networks with Higher Level Architecture for Bipolar Device Modeling
Neural Networks (NN) are potential alternative to semiconductor modeling for circuit simulation, in situations where physical modeling becomes critical. To cope with the complex behavior of a state-of-the-art bipolar device, a particular NN architecture has been developed, referred here as Higher-Level, where the main neurons are instances of differential equations, and other neurons are responsible for the coefficients of such equations. Unfortunately this type of neural architecture is difficult to train and even the most sophisticate methods often fail to converge to an acceptable error. The strategy here presented essentially reduces the problem of training the higher-level NN to model the bipolar device in all its working conditions, to the training of simpler auxiliary networks, each working at a single DC bias point.
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