Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques

Tzu Hsuan Huang; Wei Tse Hung; Hao Yu Yang; Wen Hsiang Chang; Ying Yen Chen; Chun Yi Kuo; Jih Nung Lee; Chia-Tso Chao

doi:10.1109/ASPDAC.2017.7858360

Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques

Tzu Hsuan Huang, Wei Tse Hung, Hao Yu Yang, Wen Hsiang Chang, Ying Yen Chen, Chun Yi Kuo, Jih Nung Lee, Chia-Tso Chao

Institute of Electronics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

This paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a pre-process to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.

Original language	English
Title of host publication	2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	426-431
Number of pages	6
ISBN (Electronic)	9781509015580
DOIs	https://doi.org/10.1109/ASPDAC.2017.7858360
State	Published - 16 Feb 2017
Event	22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017 - Chiba, Japan Duration: 16 Jan 2017 → 19 Jan 2017

Publication series

Name	Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC

Conference

Conference	22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
Country/Territory	Japan
City	Chiba
Period	16/01/17 → 19/01/17

Access to Document

10.1109/ASPDAC.2017.7858360

Cite this

Huang, T. H., Hung, W. T., Yang, H. Y., Chang, W. H., Chen, Y. Y., Kuo, C. Y., Lee, J. N., & Chao, C.-T. (2017). Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques. In 2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017 (pp. 426-431). Article 7858360 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASPDAC.2017.7858360

Huang, Tzu Hsuan ; Hung, Wei Tse ; Yang, Hao Yu et al. / Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques. 2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 426-431 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC).

@inproceedings{98bcde2103114501a03e29faa8be2bd0,

title = "Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques",

abstract = "This paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a pre-process to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.",

author = "Huang, {Tzu Hsuan} and Hung, {Wei Tse} and Yang, {Hao Yu} and Chang, {Wen Hsiang} and Chen, {Ying Yen} and Kuo, {Chun Yi} and Lee, {Jih Nung} and Chia-Tso Chao",

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year = "2017",

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doi = "10.1109/ASPDAC.2017.7858360",

language = "English",

series = "Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Huang, TH, Hung, WT, Yang, HY, Chang, WH, Chen, YY, Kuo, CY, Lee, JN & Chao, C-T 2017, Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques. in 2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017., 7858360, Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC, Institute of Electrical and Electronics Engineers Inc., pp. 426-431, 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017, Chiba, Japan, 16/01/17. https://doi.org/10.1109/ASPDAC.2017.7858360

Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques. / Huang, Tzu Hsuan; Hung, Wei Tse; Yang, Hao Yu et al.
2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 426-431 7858360 (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques

AU - Huang, Tzu Hsuan

AU - Hung, Wei Tse

AU - Yang, Hao Yu

AU - Chang, Wen Hsiang

AU - Chen, Ying Yen

AU - Kuo, Chun Yi

AU - Lee, Jih Nung

AU - Chao, Chia-Tso

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Y1 - 2017/2/16

N2 - This paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a pre-process to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.

AB - This paper presents a statistical model-fitting framework to efficiently decompose the impact of device Vt variation and power-network IR drop from the measured ring-oscillator frequencies without adding any extra circuitry to the original ring oscillators. The framework applies Gaussian process regression as its core model-fitting technique and stepwise regression as a pre-process to select significant predictor features. The experiments conducted based on the SPICE simulation of an industrial 28nm technology demonstrate that our framework can simultaneously predict the NMOS Vt, PMOS Vt and static IR drop of the ring oscillators based on their frequencies measured at different external supply voltages. The final resulting R squares of the predicted features are all more than 99.93%.

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Huang TH, Hung WT, Yang HY, Chang WH, Chen YY, Kuo CY et al. Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques. In 2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 426-431. 7858360. (Proceedings of the Asia and South Pacific Design Automation Conference, ASP-DAC). doi: 10.1109/ASPDAC.2017.7858360

Predicting Vt variation and static IR drop of ring oscillators using model-fitting techniques

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