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Modeling MOSFET behavior using automatic differentiation


{The electrical} attribute mannequin consists of a number of nonlinear equations. With the intention to apply AD, that is represented by a directed acyclic graph. Every vertex represents an arithmetic operation resembling 4 arithmetic operations, logarithms, and exponents, and every node represents intermediate variables. Optimizing mannequin parameters to attenuate the distinction between the calculated results of the attribute mannequin and the measured worth is just like the method of studying parameter values resembling weights and biases in a neural community. We will apply numerous environment friendly strategies developed for deep neural community to mannequin parameter extraction. Credit: Michihiro Shintani

Scientists from Nara Institute of Science and Know-how (NAIST) used the mathematical technique referred to as computerized differentiation to seek out the optimum match of experimental knowledge as much as 4 instances quicker. This analysis might be utilized to multivariable fashions of digital gadgets, which can permit them to be designed with elevated efficiency whereas consuming much less energy.

Extensive bandgap gadgets, resembling silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFET), are a crucial aspect for making converters quicker and extra sustainable. That is due to their bigger switching frequencies with smaller power losses below a variety of temperatures compared with standard silicon-based gadgets. Nevertheless, calculating the parameters that decide how the electrical current in a MOSFET responds as a operate of the utilized voltage stays troublesome in a circuit simulation. A greater strategy for becoming experimental knowledge to extract the vital parameters would supply chip producers the power to design extra environment friendly energy converters.

Now, a workforce of scientists led by NAIST has efficiently used the mathematical method referred to as computerized differentiation (AD) to considerably speed up these calculations. Whereas AD has been used extensively when coaching artificial neural networks, the present venture extends its software into the world of model parameter extraction. For issues involving many variables, the duty of minimizing the error is commonly achieved by a means of “gradient descent,” through which an preliminary guess is repeatedly refined by making small changes within the path that reduces the error the quickest. That is the place AD might be a lot quicker than earlier alternate options, resembling symbolic or numerical differentiation, at discovering path with the steepest “slope”. AD breaks down the issue into combos of fundamental arithmetic operations, every of which solely must be performed as soon as. “With AD, the partial derivatives with respect to each of the input parameters are obtained simultaneously, so there is no need to repeat the model evaluation for each parameter,” first writer Michihiro Shintani says. In contrast, symbolic differentiation offers precise options, however makes use of a considerable amount of time and computational assets as the issue turns into extra advanced.

To point out the effectiveness of this technique, the workforce utilized it to experimental knowledge collected from a commercially out there SiC MOSFET. “Our approach reduced the computation time by 3.5× in comparison to the conventional numerical-differentiation method, which is close to the maximum improvement theoretically possible,” Shintani says. This technique might be readily utilized in lots of different areas of analysis involving a number of variables, because it preserves the bodily meanings of the mannequin parameters. The applying of AD for the improved extraction of mannequin parameters will assist new advances in MOSFET growth and improved manufacturing yields.

The analysis was printed in IEEE Transactions on Energy Electronics.


Trio of tuning tools for modeling large spatial datasets


Extra info:
Michihiro Shintani et al, Accelerating Parameter Extraction of Energy MOSFET Fashions Utilizing Computerized Differentiation, IEEE Transactions on Energy Electronics (2021). DOI: 10.1109/TPEL.2021.3118057

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