Abstract
Positive bias temperature instability (PBTI) is poised to cause significant degradation to nFETs with deep scaling into nanometers. It is commonly modeled by a power law fitted with measured threshold voltage shift. For the first time, this paper shows that such models do not warrant PBTI prediction outside the stress conditions used for the fitting. The underlying cause for this failure is the errors in the extracted power exponent. Based on the understanding of different types of defects, we develop a robust as-grown-generation model and demonstrate its capability for accurate prediction of PBTI under both dc and ac conditions. The generation-induced degradation is found to play a key role. Analysis reveals that although PBTI is usually smaller than negative BTI (NBTI) within the typical test time window, it can exceed NBTI by the end of device lifetime.
Highlights
A GING has become a critical concern for CMOS technologies as scaling is reaching nanoscale regime [1]–[8]
The central objective of this paper is to develop a model for long-term positive Bias temperature instability (BTI) (PBTI) prediction under both ac and dc operation conditions
The proposed A-G model is of value only if it can predict the PBTI degradation at the low use bias and longer time, outside the range used for fitting the model parameters
Summary
A GING has become a critical concern for CMOS technologies as scaling is reaching nanoscale regime [1]–[8]. Due to the lack of wellaccepted PBTI model, the classic power law as described in (1) is widely used for lifetime prediction [13], where A, m, and n represent the prefactor, voltage, and time exponents, respectively. When measured with 1-ms delay, the time exponent declines as voltage increases [1], making it impossible for predicting the long-term PBTI under real use conditions [14]. The central objective of this paper is to develop a model for long-term PBTI prediction under both ac and dc operation conditions. Tests were carried out under constant-Eox, unless otherwise specified
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