Ex. 1: Statistical BTI Degradation of FinFETs

Due to the small device size, the doping atoms cannot be assumed to form a continuum [Sano2001], and the position of each individual doping atom has a decisive influence on the transistor’s properties. Therefore, an array of virtual devices is generated, each with a different random distribution of discrete dopants.

We examine the current/voltage characteristics for the linear and the saturated operation regime of the transistor. The threshold voltages for both regimes, V_th,lin and V_th,sat, are determined for each specific doping configuration. GTS JobServer allows for easy task setup and shows job progress and postprocessing results in a single view. An exemplary view of such a simulation is shown in Fig.1.

Ex. 2: Distributed Nano-Device Simulation

The subband-Boltzmann transport equation (BTE) is solved on slices along the channel region to determine effective mobility [Karner2015]. GTS JobServer takes care of multi-host parallelization of the subband and scattering rate calculation: Per request by the device simulator, mobility is calculated on device cuts in sub-tasks, see Fig.2.

The shift of the threshold voltage V_th is an important key figure for the transistor’s performance in an integrated circuit. A key figure is the BTI-induced V_th shift, where a small change in oxide trap distribution can cause considerable changes in V_th under device stress conditions [Cheng2010].

Large samples of trap/dopant configurations need to be investigated to achieve reasonable statistics on device reliability. A typical result of such a reliability/variability simulation (sample size 480) can be seen in Fig.3.: At the top, the BTI-induced V_th shift in an ensemble of n-FinFETs with randomly positioned oxide traps is shown; the lower plot shows ∆V_th distribution after a stress time of 105 s.

Ex. 3: Design of Experiment – Process Splits

Here, we study the effect of changes in a structure parameter (like the threshold doping) on the CV curve of a 65nm NMOS FET. We created the device from a template supplied with GTS Framework, and set up the simulation for the CV curve (f=1GHz, step V_G from -2V to +2V, E_W=-0,56V). Next, we used set up the variation of the threshold doping, which is done easily in the DOE (Design of Experiment) Script tool included in GTS Framework. Fig. 4 shows the dialogs for specifying the parameter that should vary. Of course, there can be more parameters to vary, and one can choose among various pre-defined DOE schemes (like full-factorial, full-grid, and various central composite designs) or design a custom experiment.

Obviously, parameter variation can largely benefit from parallel task execution, since the sub-tasks are independent from each other.

Fig.5 shows the resulting CV curves, for threshold dopings ranging from 0 to 1.2E19/cm³ with a step size of 4E18/cm³.

This is also a very basic example of using the DOE Scripting tool in GTS Framework – for more details, please see the DOE, Optimizer, Scripting tutorial, which explains each step in detail.