Physics based compact modeling of symmetric double gate MOS transistors with high mobility III-V channel material

Toan, Ho Le Minh; Maity, Subir Kumar

doi:10.1016/j.spmi.2021.106925

Cited by 6 publications

(2 citation statements)

References 26 publications

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“…The model predicted inversion layer capacitance (C inv ) and its validation with Schroedinger-Poisson solver result is shown in Figure-5. To demonstrate the flexibility and accuracy of our model, we have also compared ANN model predicted data with an analytical model calculated result based on an explicit solution of Schroedinger-Poisson equation for symmetric double-gate MOS-FET reported in[27]. In Figure-5, it is interesting to observe that, our ANN model predicts capacitance value with sufficient accuracy.…”

mentioning

confidence: 90%

“…But there is a large difference between analytical model calculated value and ANN model predicted data. This is because the analytical model reported in [27] considers up to 2nd subband and electron contributions from higher-order subbands are neglected. In this case, since channel thickness t ch =10nm, the electron contribution from 3rd subband can't be neglected (as shown in , where E 2 value is below 0eV for higher gate bias voltage).…”

Section: Model Validationmentioning

confidence: 99%

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Modeling of inversion layer capacitance of III-V double gate MOSFETs using a neural network-based regression technique

Maity,

Pandit

2023

J Comput Electron

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This work presents a data-driven regression model of inversion layer capacitance of double gate III-V channel MOSFETs implemented using an artificial neural network. The training dataset is generated using a Schroedinger-Poisson solver for different channel thicknesses, carrier effective masses, oxide thickness, barrier height, and a wide range of gate bias voltages. The neural network predicted capacitance value is compared with Schroedinger-Poisson solver data and a physics-based analytical model result. The model effectively captures the variation in channel thickness, barrier height, carrier effective mass, and oxide thickness. Furthermore, extensive error analysis has been performed to demonstrate the correctness and degree of accuracy of the predicted result.

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confidence: 90%

Section: Model Validationmentioning

confidence: 99%