Identification of characteristics after soft breakdown with GA-based neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this research, we analyze the low-frequency noise power spectrum of drain current (Su) in electrically stressed SiO2 film, and then propose the evolutionary neural networks-based model named ENN-SBD to identify the highly nonlinear degraded characteristics of low frequency noise around the soft breakdown (SBD). The Sid data follow the 1/f' relationship with different value of power exponent γ. The spatial oxide traps distribution is proposed to account for the different γ value. It is found that the Sid correlates closely with the gate fluctuations via the trapping and detrapping processes and hence it is feasible to build the model represents the behavior of soft breakdown. The results also indicate that ENN-SBD has more precisely identification capability than typical Lorentzian spectrum method. Besides, it is superior to the backpropagation neural networks-based model (BNN-SBD) while the system identification is proceeding. This paper is helpful for breakdown detection and saving the cost of testing from quality assurance in the process of advanced CMOS technology.

Original languageEnglish
Title of host publicationAdvances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings
PublisherSpringer Verlag
Pages564-572
Number of pages9
Volume4031 LNAI
ISBN (Print)3540354530, 9783540354536
Publication statusPublished - 2006 Jan 1
Event19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006 - Annecy, France
Duration: 2006 Jun 272006 Jun 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4031 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006
CountryFrance
CityAnnecy
Period06-06-2706-06-30

Fingerprint

Breakdown
Neural Networks
Neural networks
Low-frequency Noise
Drain current
Power spectrum
Quality assurance
Backpropagation
Evolutionary Neural Networks
Identification (control systems)
Quality Assurance
Back-propagation Neural Network
SiO2
System Identification
Oxides
Trapping
Power Spectrum
Trap
Correlate
Testing

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Wang, H-W. (2006). Identification of characteristics after soft breakdown with GA-based neural networks. In Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings (Vol. 4031 LNAI, pp. 564-572). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4031 LNAI). Springer Verlag.
Wang, Hsing-Wen. / Identification of characteristics after soft breakdown with GA-based neural networks. Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings. Vol. 4031 LNAI Springer Verlag, 2006. pp. 564-572 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{3213b34a01b04afbac3705370771e398,
title = "Identification of characteristics after soft breakdown with GA-based neural networks",
abstract = "In this research, we analyze the low-frequency noise power spectrum of drain current (Su) in electrically stressed SiO2 film, and then propose the evolutionary neural networks-based model named ENN-SBD to identify the highly nonlinear degraded characteristics of low frequency noise around the soft breakdown (SBD). The Sid data follow the 1/f' relationship with different value of power exponent γ. The spatial oxide traps distribution is proposed to account for the different γ value. It is found that the Sid correlates closely with the gate fluctuations via the trapping and detrapping processes and hence it is feasible to build the model represents the behavior of soft breakdown. The results also indicate that ENN-SBD has more precisely identification capability than typical Lorentzian spectrum method. Besides, it is superior to the backpropagation neural networks-based model (BNN-SBD) while the system identification is proceeding. This paper is helpful for breakdown detection and saving the cost of testing from quality assurance in the process of advanced CMOS technology.",
author = "Hsing-Wen Wang",
year = "2006",
month = "1",
day = "1",
language = "English",
isbn = "3540354530",
volume = "4031 LNAI",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "564--572",
booktitle = "Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings",
address = "Germany",

}

Wang, H-W 2006, Identification of characteristics after soft breakdown with GA-based neural networks. in Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings. vol. 4031 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4031 LNAI, Springer Verlag, pp. 564-572, 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, 06-06-27.

Identification of characteristics after soft breakdown with GA-based neural networks. / Wang, Hsing-Wen.

Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings. Vol. 4031 LNAI Springer Verlag, 2006. p. 564-572 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4031 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Identification of characteristics after soft breakdown with GA-based neural networks

AU - Wang, Hsing-Wen

PY - 2006/1/1

Y1 - 2006/1/1

N2 - In this research, we analyze the low-frequency noise power spectrum of drain current (Su) in electrically stressed SiO2 film, and then propose the evolutionary neural networks-based model named ENN-SBD to identify the highly nonlinear degraded characteristics of low frequency noise around the soft breakdown (SBD). The Sid data follow the 1/f' relationship with different value of power exponent γ. The spatial oxide traps distribution is proposed to account for the different γ value. It is found that the Sid correlates closely with the gate fluctuations via the trapping and detrapping processes and hence it is feasible to build the model represents the behavior of soft breakdown. The results also indicate that ENN-SBD has more precisely identification capability than typical Lorentzian spectrum method. Besides, it is superior to the backpropagation neural networks-based model (BNN-SBD) while the system identification is proceeding. This paper is helpful for breakdown detection and saving the cost of testing from quality assurance in the process of advanced CMOS technology.

AB - In this research, we analyze the low-frequency noise power spectrum of drain current (Su) in electrically stressed SiO2 film, and then propose the evolutionary neural networks-based model named ENN-SBD to identify the highly nonlinear degraded characteristics of low frequency noise around the soft breakdown (SBD). The Sid data follow the 1/f' relationship with different value of power exponent γ. The spatial oxide traps distribution is proposed to account for the different γ value. It is found that the Sid correlates closely with the gate fluctuations via the trapping and detrapping processes and hence it is feasible to build the model represents the behavior of soft breakdown. The results also indicate that ENN-SBD has more precisely identification capability than typical Lorentzian spectrum method. Besides, it is superior to the backpropagation neural networks-based model (BNN-SBD) while the system identification is proceeding. This paper is helpful for breakdown detection and saving the cost of testing from quality assurance in the process of advanced CMOS technology.

UR - http://www.scopus.com/inward/record.url?scp=33746210704&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33746210704&partnerID=8YFLogxK

M3 - Conference contribution

SN - 3540354530

SN - 9783540354536

VL - 4031 LNAI

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 564

EP - 572

BT - Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings

PB - Springer Verlag

ER -

Wang H-W. Identification of characteristics after soft breakdown with GA-based neural networks. In Advances in Applied Artificial Intelligence - 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Proceedings. Vol. 4031 LNAI. Springer Verlag. 2006. p. 564-572. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).