Prediction Model for Random Variation in FinFET Induced by Line-Edge-Roughness (LER)

Abstract

As the physical size of MOSFET has been aggressively scaled-down, the impact of process-induced random variation (RV) should be considered as one of the device design considerations of MOSFET. In this work, an artificial neural network (ANN) model is developed to investigate the effect of line-edge roughness (LER)-induced random variation on the input/output transfer characteristics (e.g., off-state leakage current (Ioff), subthreshold slope (SS), saturation drain current (Id,sat), linear drain current (Id,lin), saturation threshold voltage (Vth,sat), and linear threshold voltage (Vth,lin)) of 5 nm FinFET. Hence, the prediction model was divided into two phases, i.e., “Predict Vth” and “Model Vth”. In the former, LER profiles were only used as training input features, and two threshold voltages (i.e., Vth,sat and Vth,lin) were target variables. In the latter, however, LER profiles and the two threshold voltages were used as training input features. The final prediction was then made by feeding the output of the first model to the input of the second model. The developed models were quantitatively evaluated by the Earth Mover Distance (EMD) between the target variables from the TCAD simulation tool and the predicted variables of the ANN model, and we confirm both the prediction accuracy and time-efficiency of our model.

Process Corresponding Implications Associated with a Conclusive Model-Fit Current-Voltage Characteristic Curves

“…Nevertheless, the model, conventionally established much earlier many decades ago, has been using equivalent circuits to approach to the measured data, and enjoy many fruitful achievements [17]. But tedious work and convergence still have to be taken into account.…”

“…However, the electrical performances, mainly manifested in current-versus-voltage characteristic curves (I-V curves), are thus put to be parameter-extracted in the model, which takes advantage of sophisticated equivalent circuits for academic and industrial uses. Nevertheless, the measured I-V curves are speculated to be also fitted by the "modified" conventional formula [13][14][15][16][17][18].…”

Conclusive Algorithm with Kink Effects for Fitting 3-D FinFET and Planar MOSFET Characteristic Curves

“…The achievable function of the process is mostly due to the good conformality (step coverage) of chemical vapor deposition, in which SiH4 (silane) is controlled at a chosen flow rate and the ambient is sustained at an appropriate temperature and at certain pressure to achieve a good deposition rate in kinetic regime. [7][8][9][10][11][12][13][14][15] Somehow, the electrical performances of transistors are mainly manifested in current-versusvoltage characteristic curves. Those curves are necessarily parameter-extracted in the model, which is useful to develop circuit design.…”

Pelgrom-Based Predictive Model to Estimate Metal Grain Granularity and Line Edge Roughness in Advanced Multigate MOSFETs

“…These two factors notably increase the computational cost of these variability studies. Therefore, complementary techniques to predict variability were developed to lower the computational time, such as those based on machine learning (ML) [2], [8], [9], [10], [11], [12], the fluctuation sensitivity map (FSM) [13], the impedance field method [14], or the statistical model reported in [15].…”