Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

ML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(Δ), correlation length along x-direction (A&...

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Bibliographic Details
Main Authors: Jaehyuk Lim, Changhwan Shin
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Line edge roughness
process-induced random variation
FinFET
machine learning
artificial neural network
Online Access:https://ieeexplore.ieee.org/document/9179808/
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spelling doaj-8779d5631c264535bc6316fbb92cc72a2021-03-30T04:07:13ZengIEEEIEEE Access2169-35362020-01-01815823715824210.1109/ACCESS.2020.30200669179808Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFETJaehyuk Lim0https://orcid.org/0000-0003-1636-8865Changhwan Shin1https://orcid.org/0000-0001-6057-3773Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South KoreaDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South KoreaML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(&#x0394;), correlation length along x-direction (A<sub>X</sub>), and correlation length along y-direction (A<sub>Y</sub>), the metrics for device performance such as on-state drive current, off-state leakage current, threshold voltage, and subthreshold swing can be computing-efficiently estimated with the ANN model.https://ieeexplore.ieee.org/document/9179808/Line edge roughnessprocess-induced random variationFinFETmachine learningartificial neural network
collection DOAJ
language English
format Article
sources DOAJ
author Jaehyuk Lim
Changhwan Shin
spellingShingle Jaehyuk Lim
Changhwan Shin
Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
IEEE Access
Line edge roughness
process-induced random variation
FinFET
machine learning
artificial neural network
author_facet Jaehyuk Lim
Changhwan Shin
author_sort Jaehyuk Lim
title Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
title_short Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
title_full Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
title_fullStr Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
title_full_unstemmed Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET
title_sort machine learning (ml)-based model to characterize the line edge roughness (ler)-induced random variation in finfet
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description ML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(&#x0394;), correlation length along x-direction (A<sub>X</sub>), and correlation length along y-direction (A<sub>Y</sub>), the metrics for device performance such as on-state drive current, off-state leakage current, threshold voltage, and subthreshold swing can be computing-efficiently estimated with the ANN model.
topic Line edge roughness
process-induced random variation
FinFET
machine learning
artificial neural network
url https://ieeexplore.ieee.org/document/9179808/
work_keys_str_mv AT jaehyuklim machinelearningmlbasedmodeltocharacterizethelineedgeroughnesslerinducedrandomvariationinfinfet
AT changhwanshin machinelearningmlbasedmodeltocharacterizethelineedgeroughnesslerinducedrandomvariationinfinfet
_version_ 1724182315532288000

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