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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 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 languageEnglish
Title of host publication2017 22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages426-431
Number of pages6
ISBN (Electronic)9781509015580
DOIs
StatePublished - 16 Feb 2017
Event22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017 - Chiba, Japan
Duration: 16 Jan 201719 Jan 2017

Publication series

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

Conference

Conference22nd Asia and South Pacific Design Automation Conference, ASP-DAC 2017
CountryJapan
CityChiba
Period16/01/1719/01/17

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