Impact of Process Variation on Leakage and Drive Currents of FED Structures Using Linear Regression and Random Forest Algorithms

Panneerselvam, Sasikala; S, Sirish; Bhattacharjee, Tanushyam; Chandramani, Premanand Venkatesh; Raj, Srinivasan

doi:10.1007/s12633-023-02703-0

Silicon

2023

DOI: 10.1007/s12633-023-02703-0

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Impact of Process Variation on Leakage and Drive Currents of FED Structures Using Linear Regression and Random Forest Algorithms

Sasikala Panneerselvam,

Sirish S,

Tanushyam Bhattacharjee

et al.

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2024

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Cited by 2 publications

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A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Salah,

Yevick

2024

Advcd Theory and Sims

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This paper presents a Random Forest (RF) machine learning model that relates the DC characteristics and high‐frequency response of a carbon nanotube (CNT) tunnel field‐effect transistor (TFET) with highly doped pockets to the transistor parameters. The analysis of multiple factors for a complex structure as the one studied here becomes expensive with the ordinary simulation techniques and hence machine learning (ML) offers a proficient method to model and enhance the understanding of the key factors that influence the CNT TFET with pockets in considerably reduced time. Numerical simulations are used to generate the data on which the model is trained. This dataset comprises ten input features and four output attributes. The tuned model is capable of predicting the output characteristics of the device with minimal mean squared error (MSE). The RF model is also compared to other ML algorithms to demonstrate its advantage.

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A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Salah,

Yevick

2024

Advcd Theory and Sims

View full text Add to dashboard Cite

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Superior energy-delay-production in nanoscale field effect diode by embedded doped pockets for digital applications

Rezaei,

Orouji

2024

J Mater Sci: Mater Electron

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scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Impact of Process Variation on Leakage and Drive Currents of FED Structures Using Linear Regression and Random Forest Algorithms

Cited by 2 publications

References 20 publications

A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

A Random Forest Model for Predicting and Analyzing the Performance of CNT TFET with Highly Doped Pockets

Superior energy-delay-production in nanoscale field effect diode by embedded doped pockets for digital applications

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