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Related Concept Videos

Biasing of FET01:22

Biasing of FET

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Biasing a Junction Field Effect Transistor (JFET) is crucial for setting operational parameters and ensuring efficient functioning in electronic circuits. JFETs are characterized by using a single carrier type in N-channel or P-channel configurations, where the channel is surrounded by PN junctions. These junctions are central to the device's ability to control current flow.
In an N-channel JFET, the structure consists of N-type material forming the channel on a P-type substrate, with the...
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Characteristics of JFET01:21

Characteristics of JFET

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Junction Field Effect Transistors (JFETs) exhibit specific operational characteristics based on the relationship between the drain current (id) and the drain-source voltage (Vds), along with varying gate-source voltages (Vgs).
The core of a JFET's operation is controlling drain current by modulating the gate-source voltage. When the drain and gate voltage are set to zero, the JFET exhibits no net current flow, representing a state of equilibrium. The drain current increases linearly as the...
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Characteristics of MOSFET01:17

Characteristics of MOSFET

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Metal-oxide-semiconductor field-effect Transistors, or MOSFETs, play a critical role in electronic circuits. They are primarily utilized for amplifying and switching signals.
Various vital parameters influence their functionality, which is crucial for theory and electronics applications. First, channel dimensions, precisely length, and width, are pivotal. The size of these channels affects the transistor's ability to carry current and switching speeds; shorter channels typically enable...
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Small-Signal Analysis of MOSFET Amplifiers01:23

Small-Signal Analysis of MOSFET Amplifiers

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In small-signal analysis, a MOSFET transistor amplifier acts as a linear amplifier when operating in its saturation region. The gate-to-source voltage (VGS) of the MOSFET is the sum of the DC biasing voltage and the small time-varying input signal. This combination sets up the operating point and modulates the drain current (ID) that flows from the drain to the source. When a small AC signal is superimposed on the DC bias voltage at the gate, the instantaneous drain current comprises three...
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MOSFET01:16

MOSFET

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The Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) plays a pivotal role in modern electronics thanks to its versatility and efficiency in controlling electrical currents. This device, also known as IGFET, MISFET, and MOSFET, has three main terminals: the Source, Drain, and Gate. MOSFETs are classified into n-channel or p-channel types based on the doping characteristics of their substrate and the source or drain regions.
In an n-MOSFET, the structure includes n-type source and drain...
1.8K
Field Effect Transistor01:29

Field Effect Transistor

1.8K
Field-effect transistors (FETs) are integral to electronic circuits and distinguished by their three-terminal setup: the gate, drain, and source. These transistors operate as unipolar devices, which utilize either electrons or holes as charge carriers, in contrast to bipolar transistors, which use both types of carriers. The primary function of the FET is to modulate the flow of these carriers from the source to the drain through a channel. The voltage difference between the gate and source...
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Updated: May 7, 2026

The Effect of Anodization Parameters on the Aluminum Oxide Dielectric Layer of Thin-Film Transistors
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The Effect of Anodization Parameters on the Aluminum Oxide Dielectric Layer of Thin-Film Transistors

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Predictive modeling of drain current in advanced FET architectures using ML-based TCAD calibration.

Siddhabrata Mohapatra1, Roshni Shekhar1, Aditya Jadhav1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.

Scientific Reports
|May 5, 2026
PubMed
Summary

Machine learning models predict nanowire and nanoribbon FET drain current using TCAD data. Tree-based models like XGBoost and Random Forest outperformed deep learning, enabling faster device design.

Keywords:
ANNDevice modellingGAA-FETMachine learningNanowire FETSub 10 nmTCADTrap charges

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Area of Science:

  • Semiconductor device physics and engineering
  • Computational electronics and materials science
  • Machine learning applications in nanotechnology

Background:

  • Advanced semiconductor devices like multi-gate MOSFETs are crucial for integrated circuit miniaturization.
  • Accurate modeling of transistor behavior is essential but computationally challenging for sub-10nm geometries.
  • Existing methods often require extensive Technology Computer-Aided Design (TCAD) simulations.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) driven approach for predicting the drain current (Id) of nanowire and nanoribbon field-effect transistors (FETs).
  • To compare the performance of various ML models, including tree-based and deep learning methods, using TCAD-extracted datasets.
  • To reduce the computational burden of traditional TCAD simulations for device characterization.

Main Methods:

  • Extracted datasets from TCAD simulations were used to train six ML models: XGBoost, Random Forest, 1D Convolutional Neural Network (CNN), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and TabNet.
  • Model performance was evaluated using metrics such as R-squared (R²) and Root Mean Squared Error (RMSE).
  • A web-based application was developed to deploy the best-performing models for real-time prediction and visualization.

Main Results:

  • Ensemble tree-based methods, XGBoost and Random Forest, demonstrated superior performance in predicting drain current.
  • XGBoost achieved an R² score of 0.9926 and RMSE of 2.1×10⁻⁴ A, while Random Forest achieved an R² of 0.9549 and RMSE of 2.0×10⁻⁴ A.
  • Deep learning models like LSTM (R² = 0.6778) and 1D CNN (R² = 0.6997) showed significantly lower accuracy, indicating tree-based models generalize better for structured TCAD data.

Conclusions:

  • Tree-based ML models are highly effective for predicting semiconductor device behavior using TCAD data, challenging the assumption that deep learning is always superior.
  • The proposed ML framework significantly accelerates the prediction of device characteristics, reducing reliance on iterative TCAD simulations.
  • The developed web application provides a practical tool for real-time drain current prediction and visualization, aiding in rapid device design and optimization.