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The electrical characteristic model is composed of multiple nonlinear equations. In order to apply AD, this is represented by a directed acyclic graph. Each vertex represents an arithmetic operation such as four arithmetic operations, logarithms, and exponents, and each node represents intermediate variables. Optimizing model parameters to minimize the difference between the calculated result of the characteristic model and the measured value is similar to the process of learning parameter values such as weights and biases in a neural network. We can apply various efficient methods developed for deep neural network to model parameter extraction.
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Michihiro Shintani
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