Analog performance of double gate SOI transistors

Double gate fully depleted silicon-on-insulator (DGSOI) is recognized as a possible solution when the physical gate length L-G reduces to 25nm for the 65nm node on the ITRS CMOS roadmap. In this paper, scaling guidelines are introduced to optimally design a nanoscale DGSOI. For this reason, the sensitivity of gain, f(T) and f(max) to each of the key geometric and technological parameters of the DGSOI are assessed and quantified using MixedMode simulation. The impact of the parasitic resistance and capacitance on analog device performance is systematically analysed. By comparing analog performance with a single gate (SG), it has been found that intrinsic gain in DGSOI is 4 times higher but its fT was found to be comparable to that of SGSOI at different regions of transistor operation. However, the extracted fmax in SG SOI was higher (similar to 40%) compared to DGSOI due to its lower capacitance.

Extending BPM Environments of Your Choice with Performance Related Decision Support

What-if Simulations have been identified as one solution for business performance related decision support. Such support is especially useful in cases where it can be automatically generated out of Business Process Management (BPM) Environments from the existing business process models and performance parameters monitored from the executed business process instances. Currently, some of the available BPM Environments offer basic-level performance prediction capabilities. However, these functionalities are normally too limited to be generally useful for performance related decision support at business process level. In this paper, an approach is presented which allows the non-intrusive integration of sophisticated tooling for what-if simulations, analytic performance prediction tools process optimizations or a combination Of Such solutions into already existing BPM environments. The approach abstracts from process modelling techniques which enable automatic decision support spanning processes across numerous BPM Environments. For instance, this enables end-to-end decision support for composite processes modelled with the Business Process Modelling Notation (BPMN) on top of existing Enterprise Resource Planning (ERP) processes modelled with proprietary languages.