NDLI: Remembrance of transistors past: Compact model parameter extraction using bayesian inference and incomplete new measurements

Content Provider	IEEE Xplore Digital Library
Author	Li Yu Saxena, S. Hess, C. Elfadel, A. Antoniadis, D. Boning, D.
Copyright Year	2014
Description	Author affiliation: Masdar Inst. of Sci. & Technol., Abu Dhabi, United Arab Emirates (Elfadel, A.) \|\| Massachusetts Inst. of Technol., Cambridge, MA, USA (Li Yu; Antoniadis, D.; Boning, D.) \|\| PDF Solutions, USA (Saxena, S.; Hess, C.)
Abstract	In this paper, we propose a novel MOSFET parameter extraction method to enable early technology evaluation. The distinguishing feature of the proposed method is that it enables the extraction of an entire set of MOSFET model parameters using limited and incomplete IV measurements from on-chip monitor circuits. An important step in this method is the use of maximum-a-posteriori estimation where past measurements of transistors from various technologies are used to learn a prior distribution and its uncertainty matrix for the parameters of the target technology. The framework then utilizes Bayesian inference to facilitate extraction using a very small set of additional measurements. The proposed method is validated using various past technologies and post-silicon measurements for a commercial 28-nm process. The proposed extraction could also be used to characterize the statistical variations of MOSFETs with the significant benefit that some constraints required by the backward propagation of variance (BPV) method are relaxed.
Starting Page	1
Ending Page	6
File Size	1384521
Page Count	6
File Format	PDF
ISBN	9781479930173
DOI	10.1145/2593069.2593201
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Transistors Current measurement Measurement uncertainty Semiconductor device modeling Parameter extraction Uncertainty Mathematical model maximum-a-posteriori (MAP) estimation MIT Virtual Source (MVS) MOSFET model parameter extraction Bayesian inference
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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