Toward more energy efficient power converters

Date:
October 12, 2021
Source:
Nara Institute of Science and Technology
Summary:
Researchers extend the mathematical approach called automatic
differentiation from machine learning to the fitting of
model parameters that describe the behavior of field-effect
transistors. This allowed the parameters to be extracted up to
3.5 times faster compared with previous methods, which may lead
to more sustainable microelectronics.



FULL STORY ========================================================================== Scientists from Nara Institute of Science and Technology (NAIST) used the mathematical method called automatic differentiation to find the optimal
fit of experimental data up to four times faster. This research can be
applied to multivariable models of electronic devices, which may allow
them to be designed with increased performance while consuming less power.


==========================================================================
Wide bandgap devices, such as silicon carbide (SiC) metal-oxide
semiconductor field-effect transistors (MOSFET), are a critical element
for making converters faster and more sustainable. This is because of
their larger switching frequencies with smaller energy losses under a
wide range of temperatures when compared with conventional silicon-based devices. However, calculating the parameters that determine how the
electrical current in a MOSFET responds as a function of the applied
voltage remains difficult in a circuit simulation. A better approach
for fitting experimental data to extract the important parameters would
provide chip manufacturers the ability to design more efficient power converters.

Now, a team of scientists led by NAIST has successfully used the
mathematical method called automatic differentiation (AD) to significantly accelerate these calculations. While AD has been used extensively when
training artificial neural networks, the current project extends its application into the area of model parameter extraction. For problems
involving many variables, the task of minimizing the error is often accomplished by a process of "gradient descent," in which an initial guess
is repeatedly refined by making small adjustments in the direction that
reduces the error the quickest. This is where AD can be much faster than previous alternatives, such as symbolic or numerical differentiation, at finding direction with the steepest "slope." AD breaks down the problem
into combinations of basic arithmetic operations, each of which only
needs to be done once. "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 author Michihiro Shintani says. By contrast, symbolic differentiation provides
exact solutions, but uses a large amount of time and computational
resources as the problem becomes more complex.

To show the effectiveness of this method, the team applied it
to experimental data collected from a commercially available SiC
MOSFET. "Our approach reduced the computation time by 3.5x in comparison
to the conventional numerical- differentiation method, which is close
to the maximum improvement theoretically possible," Shintani says. This
method can be readily applied in many other areas of research involving multiple variables, since it preserves the physical meanings of the model parameters. The application of AD for the enhanced extraction of model parameters will support new advances in MOSFET development and improved manufacturing yields.

========================================================================== Story Source: Materials provided by
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========================================================================== Journal Reference:
1. Michihiro Shintani, Aoi Ueda, Takashi Sato. Accelerating Parameter
Extraction of Power MOSFET Models Using Automatic
Differentiation. IEEE Transactions on Power Electronics, 2021;
1 DOI: 10.1109/TPEL.2021.3118057 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211012112313.htm

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