Modeling MOSFET habits utilizing computerized differentiation

{The electrical} attribute mannequin consists of a number of nonlinear equations. With a view to apply AD, that is represented by a directed acyclic graph. Every vertex represents an arithmetic operation equivalent to 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 equivalent to weights and biases in a neural community. We are able to apply numerous environment friendly strategies developed for deep neural community to mannequin parameter extraction. Credit score: Michihiro Shintani

Scientists from Nara Institute of Science and Expertise (NAIST) used the mathematical technique referred to as computerized differentiation to search out the optimum match of experimental information as much as 4 occasions quicker. This analysis could be utilized to multivariable fashions of digital units, which can permit them to be designed with elevated efficiency whereas consuming much less energy.

Huge bandgap units, equivalent to silicon carbide (SiC) metal-oxide semiconductor field-effect transistors (MOSFET), are a important factor for making converters quicker and extra sustainable. That is due to their bigger switching frequencies with smaller vitality losses underneath a variety of temperatures compared with standard silicon-based units. Nonetheless, calculating the parameters that decide how {the electrical} present in a MOSFET responds as a perform of the utilized voltage stays troublesome in a circuit simulation. A greater strategy for becoming experimental information to extract the vital parameters would supply chip producers the flexibility to design extra environment friendly energy converters.

Now, a workforce of scientists led by NAIST has efficiently used the mathematical technique referred to as computerized differentiation (AD) to considerably speed up these calculations. Whereas AD has been used extensively when coaching synthetic neural networks, the present challenge extends its utility into the world of mannequin parameter extraction. For issues involving many variables, the duty of minimizing the error is usually achieved by a means of “gradient descent,” through which an preliminary guess is repeatedly refined by making small changes within the route that reduces the error the quickest. That is the place AD could be a lot quicker than earlier alternate options, equivalent to symbolic or numerical differentiation, at discovering route with the steepest “slope”. AD breaks down the issue into mixtures of fundamental arithmetic operations, every of which solely must be completed as soon as. “With AD, the partial derivatives with respect to every of the enter parameters are obtained concurrently, so there isn’t a must repeat the mannequin analysis for every parameter,” first creator Michihiro Shintani says. In contrast, symbolic differentiation gives actual 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 information collected from a commercially accessible SiC MOSFET. “Our strategy lowered the computation time by 3.5× compared to the standard numerical-differentiation technique, which is near the utmost enchancment theoretically potential,” Shintani says. This technique could 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 help new advances in MOSFET improvement and improved manufacturing yields.

The analysis was revealed in IEEE Transactions on Energy Electronics.


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Extra data:
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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