Creation of a behavioral model of a LDMOS transistor based on an artificial MLP neural network and its description in Verilog-A language

Author:

Pobeda Sergey1,Chernyh M.2,Makarenko F.3,Zolnikov Konstantin2

Affiliation:

1. AO "NIIET"

2. AO "Nauchno-issledovatel'skiy institut elektronnoy tehniki"

3. AO "Nauchno-issledovatel'skiy institut elektronnoy tehniki" (g. Voronezh)

Abstract

The article deals with the creation of a behavioral model of lateral metal oxide transistors (LDMOS) based on a neural network of the multilayer percep-tron type. The model is identified using a backpropa-gation algorithm. Demonstrated the process of creating an ANN model using Pytorch, a machine learning framework for the Python language, with subsequent transfer to the standard analog circuit modeling lan-guage Verilog-A.

Publisher

Infra-M Academic Publishing House

Subject

General Medicine

Reference30 articles.

1. Tsividis, Y. Operation and Modeling of the MOS Transistor / Y. Tsividis. - McGraw-Hill, New York,1999. – 723 p., Tsividis, Y. Operation and Modeling of the MOS Transistor / Y. Tsividis. - McGraw-Hill, New York,1999. – 723 p.

2. Khakifirooz, A. A simple semiempirical short-channel MOSFET current–voltage model continuous across all regions of operation and employing only physical parameters / A. Khakifirooz, O.M. Nayfeh, D. Antoniadis // IEEE Transactions on Electron Devices. – 2009. – Т. 56, № 8. – Pp. 1674-1680., Khakifirooz, A. A simple semiempirical short-channel MOSFET current–voltage model continuous across all regions of operation and employing only physical parameters / A. Khakifirooz, O.M. Nayfeh, D. Antoniadis // IEEE Transactions on Electron Devices. – 2009. – T. 56, № 8. – Pp. 1674-1680.

3. Root, D.E. The large-signal model: Theoretical foundations, practical considerations, and recent trends / D.E. Root [et al.] //Nonlinear Transistor Model Parameter Extraction Technique. – 2011. – Pp. 123-170., Root, D.E. The large-signal model: Theoretical foundations, practical considerations, and recent trends / D.E. Root [et al.] //Nonlinear Transistor Model Parameter Extraction Technique. – 2011. – Pp. 123-170.

4. Zhang, Q.J. Artificial neural networks for RF and microwave design-from theory to practice / Q.J. Zhang, K.C. Gupta, V.K. Devabhaktuni // IEEE transactions on microwave theory and techniques. – 2003. – Т. 51, № 4. – Pp. 1339-1350., Zhang, Q.J. Artificial neural networks for RF and microwave design-from theory to practice / Q.J. Zhang, K.C. Gupta, V.K. Devabhaktuni // IEEE transactions on microwave theory and techniques. – 2003. – T. 51, № 4. – Pp. 1339-1350.

5. Feng, F. Multifeature-assisted neuro-transfer function surrogate-based EM optimization exploiting trust-region algorithms for microwave filter design / F. Feng [et al.] // IEEE Transactions on Microwave Theory and Techniques. – 2019. – Т. 68, № 2. – Pp. 531-542., Feng, F. Multifeature-assisted neuro-transfer function surrogate-based EM optimization exploiting trust-region algorithms for microwave filter design / F. Feng [et al.] // IEEE Transactions on Microwave Theory and Techniques. – 2019. – T. 68, № 2. – Pp. 531-542.

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