Abstract
Fluctuation effects are becoming important in advanced VLSI devices because of their increasing impact on circuit performance and chip yields. Accurate modelling of these effects generally requires full 3D simulation, which is used here to analyse four of the primary such effects. Polysilicon line edge roughness causes excess device leakage, which can be reduced at the cost of decreased performance. Phase-shift mask defects can reduce current drive and increase capacitance. Random dopant fluctuation, which causes variation in threshold voltage, is evaluated for three technology generations and it is shown that proper tip scaling can reduce these variations. Finally, a study of alpha particle strikes evaluates the effectiveness of SOI in improving soft error reliability.
Highlights
Continued VLSI device scaling is increasing the importance of statistical fluctuation phenomena
This is because decreasing size increases the relative size of fluctuations at the same time that the larger number of devices on a chip raises the number of devices which are in the "tail" of the device distribution and which show large deviations from the nominal characteristics
This paper addresses four types of device variation that are becoming important: MOS gate line edge roughness (LER), phase-shift mask defects (MD), random dopant fluctuation (RDF), and soft error reliability (SER)
Summary
Continued VLSI device scaling is increasing the importance of statistical fluctuation phenomena. Would expect, since the current through the short part of the device will increase faster than the current through the long half will decrease, as a consequence of the superlinear dependence of IOFF on inverse channel length. For LGATE 80 nm, 12 nm LER requires a gate length increase of 3.5 nm, resulting in 5.4% ION degradation, 4.4% increase in gate capacitance, and net performance degradation of about 9.4%. These results may be used to help set lithographic tolerances. An atomistic simulation [3,4] approach was used with full 3D single-carrier device simulation of 500 fluctuated devices for each device structure under study
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