In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure. The tested devices are based on the multifinger layout, in which the total gate width (W) is obtained by multiplying the number of fingers (Nf) and their length (W0). Machines are trained with scattering (S-)parameter measurements up to 65 GHz by using the extreme gradient boosting (XGBoost) algorithm with K-fold cross-validation. Then, the output of the trained machine is utilized by the parameters such as Nf and W0 inside the Auto-encoder (AE) model. In particular, the ML model with AE has a maximum of 99.88% prediction accuracy, despite the uncertainty inherent in the microwave measurements and the unavoidable deviations from the ideal behavior of the analyzed devices.

Bhargava, G., Vadalà, V., Majumdar, S., Crupi, G. (2022). Auto-encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices. INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, 1-8 [10.1002/mmce.23339].

Auto-encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices

Vadalà, V
;
2022

Abstract

In this article, a study of performing machine learning (ML) based modeling for semiconductor devices has been developed using experimental microwave data. Characterization of gallium arsenide (GaAs) pseudomorphic high electron mobility transistors (pHEMTs) with different gate widths is used as the illustrative example to demonstrate the accuracy and effectiveness of the presented modeling procedure. The tested devices are based on the multifinger layout, in which the total gate width (W) is obtained by multiplying the number of fingers (Nf) and their length (W0). Machines are trained with scattering (S-)parameter measurements up to 65 GHz by using the extreme gradient boosting (XGBoost) algorithm with K-fold cross-validation. Then, the output of the trained machine is utilized by the parameters such as Nf and W0 inside the Auto-encoder (AE) model. In particular, the ML model with AE has a maximum of 99.88% prediction accuracy, despite the uncertainty inherent in the microwave measurements and the unavoidable deviations from the ideal behavior of the analyzed devices.
Articolo in rivista - Articolo scientifico
auto-encoder; GaAs pHEMT; machine learning; multifinger layout; scattering parameter measurements; semiconductor device modeling;
English
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Bhargava, G., Vadalà, V., Majumdar, S., Crupi, G. (2022). Auto-encoder based hybrid machine learning model for microwave scaled GaAs pHEMT devices. INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, 1-8 [10.1002/mmce.23339].
Bhargava, G; Vadalà, V; Majumdar, S; Crupi, G
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/393648
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