Based on 3D statistical device simulation, the impacts of key statistical variability (SV) sources (in both individual and combined forms) on device characteristics are studied in detail for a 32 nm thin-body SOI technology. The corresponding impacts on SRAM cell stability are presented as well. The simulation results indicate that thin body architectures are not only resistant to random discreet dopant induced variation, but also less sensitive to length edge roughness induced variation
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
Conventional bulk CMOS, which is arguably most vulnerable to statistical variability (SV), is the wo...
The effects of intrinsic parameter fluctuations, including line-edge-roughness (LER), silicon-body t...
Intrinsic parameter fluctuations steadily increases with CMOS technology scaling. Around the 90nm te...
This paper presents a comprehensive statistical variability study of 14-nm technology node SOI FinFE...
The CMOS scaling increases the impact of intrinsic parameter fluctuation on the yield and functional...
As CMOS device dimensions are being aggressively scaled, the device characteristic must be assessed ...
This paper presents an evaluation of 14-nm SOI FinFET CMOS SRAM codesign techniques in the presence ...
The quantitative evaluation of the impact of key sources of static and dynamic statistical variabili...
Statistical variability in ultra-scaled CMOS devices is a major challenge faced by the semiconductor...
Variability is a critical concern for the stability and yield of SRAM with minimized size. We prese...
A comprehensive study of statistical variability (SV) in scaled, fully-depleted (FD) SOI n-channel M...
Simulations of up to 10 000 fully depleted thin-body silicon-on-insulator MOSFETs show that the stan...
3-D mixed-mode device-circuit simulation is presented to investigate stochastic mismatch of FinFETs ...
The SRAM has a very constrained cell area and is consequently sensitive to the intrinsic parameter f...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
Conventional bulk CMOS, which is arguably most vulnerable to statistical variability (SV), is the wo...
The effects of intrinsic parameter fluctuations, including line-edge-roughness (LER), silicon-body t...
Intrinsic parameter fluctuations steadily increases with CMOS technology scaling. Around the 90nm te...
This paper presents a comprehensive statistical variability study of 14-nm technology node SOI FinFE...
The CMOS scaling increases the impact of intrinsic parameter fluctuation on the yield and functional...
As CMOS device dimensions are being aggressively scaled, the device characteristic must be assessed ...
This paper presents an evaluation of 14-nm SOI FinFET CMOS SRAM codesign techniques in the presence ...
The quantitative evaluation of the impact of key sources of static and dynamic statistical variabili...
Statistical variability in ultra-scaled CMOS devices is a major challenge faced by the semiconductor...
Variability is a critical concern for the stability and yield of SRAM with minimized size. We prese...
A comprehensive study of statistical variability (SV) in scaled, fully-depleted (FD) SOI n-channel M...
Simulations of up to 10 000 fully depleted thin-body silicon-on-insulator MOSFETs show that the stan...
3-D mixed-mode device-circuit simulation is presented to investigate stochastic mismatch of FinFETs ...
The SRAM has a very constrained cell area and is consequently sensitive to the intrinsic parameter f...
It has been shown that sub 100nm SRAM is particularly sensitive to stochastic device variability. In...
Conventional bulk CMOS, which is arguably most vulnerable to statistical variability (SV), is the wo...
The effects of intrinsic parameter fluctuations, including line-edge-roughness (LER), silicon-body t...