A physics-based compact model has been developed to predict DC and AC Bias Temperature Instability (BTI) induced threshold voltage shift (ΔVT) in HKMG MOSFETs. For Negative BTI (NBTI) in p-MOSFETs, the model uses Si/IL interface trap generation (ΔVIT-IL) and hole trapping in IL bulk (ΔVIT-IL). For Positive BTI (PBTI) in n-MOSFETs, it uses IL/HK interface trap generation (ΔVIT-HK) and electron trapping in HK bulk (ΔVET-HK). The model framework has been extended to generate device level stochastic ΔVT distributions and eventually VT distributions by taking time zero variability into account. VT distributions with stress time for DC and AC stress and for different duty cycles for AC stress are investigated. The resulti...
A common framework of trap generation and trapping is used to explain Negative Bias Temperature Inst...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...
BSIM-CMG based HSPICE framework is developed for simulating time-zero and Negative Bias Temperature ...
Bias Temperature Instability (BTI) is a major reliability issue in Nano-Scale CMOS Circuits. BTI eff...
A physics-based modeling framework is proposed to calculate the threshold voltage shift (Delta V-T) ...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
A physics-based modeling framework is proposed to calculate the threshold voltage shift (ΔV<sub...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
Defects, both as-fabricated and generated during operation, are an inevitable reality of real-world ...
A comprehensive modeling framework involving mutually uncorrelated contribution from interface trap ...
We investigate the impact of negative-bias temperature instability (NBTI) on the degradation of the ...
Consistency of the recently proposed deterministic composite modeling framework for Negative Bias Te...
A physical modeling framework is demonstrated for Negative Bias Temperature Instability (NBTI). It c...
Consistency of the recently proposed deterministic composite modeling framework for Negative Bias Te...
A common framework of trap generation and trapping is used to explain Negative Bias Temperature Inst...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...
BSIM-CMG based HSPICE framework is developed for simulating time-zero and Negative Bias Temperature ...
Bias Temperature Instability (BTI) is a major reliability issue in Nano-Scale CMOS Circuits. BTI eff...
A physics-based modeling framework is proposed to calculate the threshold voltage shift (Delta V-T) ...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
A physics-based modeling framework is proposed to calculate the threshold voltage shift (ΔV<sub...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
Defects, both as-fabricated and generated during operation, are an inevitable reality of real-world ...
A comprehensive modeling framework involving mutually uncorrelated contribution from interface trap ...
We investigate the impact of negative-bias temperature instability (NBTI) on the degradation of the ...
Consistency of the recently proposed deterministic composite modeling framework for Negative Bias Te...
A physical modeling framework is demonstrated for Negative Bias Temperature Instability (NBTI). It c...
Consistency of the recently proposed deterministic composite modeling framework for Negative Bias Te...
A common framework of trap generation and trapping is used to explain Negative Bias Temperature Inst...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...
session 2: MemoryInternational audienceThe paper presents a new methodology to model the dynamic var...