Abstract
A new approach for modeling low frequency noise is presented to enable the predictions of noise behavior from negative bias temperature instability (NBTI). The noise model is based on a capture-emission energy (CEE) map describing the probability density function of widely distributed defect capture-emission activation energies. To enlarge the capture-emission energy window and to perform the accurate estimation of the recoverable component of CEE, the Gaussian mixture model (GMM) is applied to the CEE map. This approach provides an efficient identification of noise sources and an in-depth noise analysis under both stationary and cyclo-stationary conditions.
Highlights
It has been found that the recoverable component of negative bias temperature instability (NBTI) and random telegraph signal (RTS) noise are due to same defects [19,20,21,22,23,24,25]
Given that the trap distribution is described by the normalized density Dr∗ ( e τc (Ec), e τe (Ee) ) of the recoverable component, the average noise power spectrum for a device with a width W and length L is converted to an integral expression
The devices used for measurements were fabricated using a manufacturable remote plasma nitride oxide (RPNO) process
Summary
It has been found that the recoverable component of NBTI and random telegraph signal (RTS) noise are due to same defects [19,20,21,22,23,24,25]. For the statistical analysis and rigorous modeling of noise behavior, it is crucial to incorporate the distribution of defects based on CEE maps, including large signal AC operations [27,28,29,30]. In the CEE map framework, the time-dependent variability of degradation such as NBTI and noise is reproduced under different bias and duty cycle conditions. The models utilize a Gaussian mixture model (GMM) for enlarging the capture-emission energy window and clustering the recoverable component from the fitted CEE map
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