New method for the automated massive characterization of Bias Temperature Instability in CMOS transistors
P. Saraza-Canflanca1, J. Diaz-Fortuny2, R. Castro-Lopez1, E. Roca1, J. Martin-Martinez2, R. Rodriguez2, M. Nafria2 and F. V. Fernandez1,a
1Instituto de Microelectrónica de Sevilla, IMSE-CNM (CSIC/Universidad de Sevilla), Sevilla, Spain
afrancisco.fernandez@imse-cnm.csic.es
2Electronic Engineering Department (REDEC) group, Universitat Autònoma de Barcelona (UAB) Barcelona, Spain
ABSTRACT
Bias Temperature Instability has become a critical issue for circuit reliability. This phenomenon has been found to have a stochastic and discrete nature in nanometerscale CMOS technologies. To account for this random nature, massive experimental characterization is necessary so that the extracted model parameters are accurate enough. However, there is a lack of automated analysis tools for the extraction of the BTI parameters from the extensive amount of generated data in those massive characterization tests. In this paper, a novel algorithm that allows the precise and fully automated parameter extraction from experimental BTI recovery current traces is presented. This algorithm is based on the Maximum Likelihood Estimation principles, and is able to extract, in a robust and exact manner, the threshold voltage shifts and emission times associated to oxide trap emissions during BTI recovery, required to properly model the phenomenon.
Keywords: Reliability, Aging, Characterization, Maximum Likelihood Estimation, Time Dependent Variability, Bias Temperature Instability.
