A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models

Javier Diaz-Fortuny, Javier Martin-Martinez, Rosana Rodriguez, Rafael Castro-Lopez, Elisenda Roca, Francisco V. Fernandez, Montserrat Nafria

Research output: Contribution to journalArticleResearch

1 Citation (Scopus)

Abstract

© 2019 Elsevier Ltd In modern nanometer-scale CMOS technologies, time-zero and time-dependent variability (TDV) effects, the latter coming from aging mechanisms like Bias Temperature Instability (BTI), Hot Carrier Injection (HCI) or Random Telegraph Noise (RTN), have re-emerged as a serious threat affecting the performance of analog and digital integrated circuits. Variability induced by the aging phenomena can lead circuits to a progressive malfunction or failure. In order to understand the effects of the mentioned variability sources, a precise and sound statistical characterization and modeling of these effects should be done. Typically, transistor TDV characterization entails long, and typically prohibitive, testing times, as well as huge amounts of data, which are complex to post-process. In order to face these limitations, this work presents a new method to statistically characterize the emission times and threshold voltage shifts (ΔVth) related to oxide defects in nanometer CMOS transistors during aging tests. At the same time, the aging testing methodology significantly reduces testing times by parallelizing the stress. The method identifies the Vth drops associated to oxide trap emissions during BTI and HCI aging recovery traces while removing RTN and background noise contributions, to avoid artifacts during data analysis.
Original languageEnglish
Pages (from-to)99-105
JournalSolid-State Electronics
Volume159
DOIs
Publication statusPublished - 1 Sep 2019

Keywords

  • Aging
  • BTI
  • CMOS
  • Defects
  • Extraction
  • HCI
  • Method
  • Parameters
  • RTN

Fingerprint Dive into the research topics of 'A smart noise- and RTN-removal method for parameter extraction of CMOS aging compact models'. Together they form a unique fingerprint.

  • Cite this