Zhang, JF, Ji, Z and Zhang, WEI (2017) As-grown-Generation (AG) Model of NBTI: a shift from fitting test data to prediction. Microelectronics Reliability. ISSN 0026-2714

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Abstract

Negative bias temperature instabilities (NBTI) received little attention pre-2000, but have been intensively investigated post-2000, as they become limiting device lifetime. The relatively thick oxides and low electrical field used in the pre-2000 works make hole injection into oxides negligible. In contrast, hole injection is substantial for most of the post-2000 research that used thin oxides and high fields. This leads to a number of discrepancies between pre- and post-2000 works, in terms of kinetics, recovery, and relative contribution of different types of defects. To account for these discrepancies, a number of models have been proposed. Although these models can fit accelerated test data well, evidences are not enough to convince that they can predict the long term NBTI under low use-Vdd. This article first reviews the discrepancies between pre- and post-2000 works and then uses the Reaction-Diffusion framework as an example to show its inability of prediction for general processes. Evidences for the presence of both As-grown Hole Traps (AHTs) and Generated Defects (GDs) during typical NBTI stresses are presented and techniques for their separation are described. This lays the foundation for the As-grown-Generation (AG) model and its prediction capability will be demonstrated, followed by an analysis why AG model can predict, while others cannot. Finally, speculations are made on the mechanism, defects, and damaging species in terms of holes, hydrogenous species, and their interactions. Although atomic structures of defects are not known, some conditions for their candidates are given.

Item Type:	Article
Uncontrolled Keywords:	0906 Electrical And Electronic Engineering
Subjects:	T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Electronics & Electrical Engineering (merged with Engineering 10 Aug 20)
Publisher:	Elsevier
Date Deposited:	28 Nov 2017 15:21
Last Modified:	04 Sep 2021 03:34
URI:	https://researchonline.ljmu.ac.uk/id/eprint/7616

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