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Thermal and electrical transport in FinFET with statistical variations is investigated by 3D coupled electro-thermal simulation, using statistical-variability-aware device simulator GARAND with built-in thermal simulation module. The module employs a new formula for the calculation of the thermal conductivity in the fin region with fin shape dependence. An(More)
Introduction Increasing CMOS device variability has become one of the most acute problems facing the semiconductor manufacturing and design industries at, and beyond, the 45 nm technology generation. Most problematic of all is the statistical variability introduced by the discreteness of charge and granularity of matter in transistors with features already(More)
This paper presents a principal component analysis (PCA)-based unified compact modelling strategy for process-induced and statistical variability in 14-nm double gate SOI FinFET technology. There is strong interplay between process and statistical variability in FinFET technology and failure to capture the correlations between them can lead to an inaccurate(More)
Device parameter fluctuations - which arise from both the stochastic nature of the manufacturing process, and more fundamentally from the intrinsic discreteness of charge and matter - have become a dominant source of device mismatch in the deca-nanometer regime, and are recognised as a crucial bottleneck to the future yield and performance of circuits and(More)
Keywords: Statistical variability Trapped charge Thin-body devices Random discrete dopant Line edge roughness a b s t r a c t The quantitative evaluation of the impact of key sources of static and dynamic statistical variability (SV) are presented for LSTP nMOSFETs corresponding to 32 nm and 22 nm technology generation transistors with thin-body (TB) SOI(More)
Novel device architectures such as ultra-thin body silicon-on-insulator (UTB SOI) MOSFETs which are more resistant to some of the sources of intrinsic parameter fluctuations are expected to play an increasingly important role beyond the 45 nm technology node. Apart from reduced device variability UTB SOI SRAM would benefit considerably from the superior(More)
² The TRAMS (Terascale Reliable Adaptive MEMORY Systems) project addresses in an evolutionary way the ultimate CMOS scaling technologies and paves the way for revolutionary, most promising beyond-CMOS technologies. In this abstract we show the significant variability levels of future 18 and 13nm device bulk-CMOS technologies as well as its dramatic effect(More)
Ultra Thin Body (UTB) SOI MOSFETs are increasingly competitive for nanometre scale VLSI applications due to superior electrostatic integrity compared to conventional MOSFETs. The possibility to use undoped channels in such devices also dramatically reduces the random dopant induced parameter fluctuations. To fully realise performance benefits of UTB SOI(More)