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Zhang, JF, Ji, Z and Zhang, WD (2017) The As-grown-Generation (AG) model: A reliable model for reliability prediction under real use conditions. In: 2017 IEEE 24th International Symposium on the Physical and Failure Analysis of Integrated Circuits (IPFA) . (IEEE 24th International Symposium on The Physical and Failure Analysis of Integrated Circuits, 04 July 2017 - 07 July 2017, Chengdu).
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Abstract
Modeling the negative bias temperature instability (NBTI) can optimize circuit design. Several models have been proposed and all of them can fit test data well. These models are extracted typically by fitting short accelerated stress data. Their capability to predict NBTI aging outside the test range has not been fully demonstrated. This predictive capability for long term aging under low operation bias is what needed by circuit designers. In this work, we test the predictive capability of the well-known reaction-diffusion (RD) based framework for samples fabricated by a variety of processes. Results show that the RD model cannot make an acceptable generic prediction. The recently proposed As-grown-Generation (AG) model is then introduced. By dividing defects into two groups, as-grown and generated defects, and measuring the as-grown defects experimentally, we demonstrate that it can make reliable prediction for the same set of data where the RD model failed.
| Item Type: | Conference or Workshop Item (Paper) |
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| Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering |
| Divisions: | Electronics & Electrical Engineering (merged with Engineering 10 Aug 20) |
| Publisher: | IEEE |
| Date Deposited: | 07 Aug 2017 11:51 |
| Last Modified: | 08 Jul 2024 14:53 |
| DOI or ID number: | 10.1109/IPFA.2017.8060059 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/6891 |
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