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.
Highlights
The Earth Mover Distance (EMD) scores are obtained by comparing the test data and the prediction data
Considering the long running-time issue in Technology Computer-Aided Design (TCAD) simulation, we suggest that the artificial neural network (ANN) model can be a promising alternative to TCAD simulation, when it comes to predicting the line-edge roughness (LER)-induced random variation in FinFET
We have proposed and developed an Artificial Neural Network (ANN) model to predict the LER-induced variation of the Id–Vg curve of 5 nm FinFET
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The. LER profile can be characterized (and can be reconfigured) with three parameters, i.e., RMS amplitude (σ), correlation length (ξ), and roughness exponent (α) [7,8,9,10,11]. The roughness exponent is defined as a fractal dimension This indicates the amount of high-frequency components left behind in LER profile [5,12,13]. We set the LER profiles as a training input feature and specify the characteristic parameters of Id–Vg curve of the device as target variables, so that perceptrons in each layer of the ANN model can learn the coefficient between them. If there is a model (which has been trained with various LER profiles), it can predict the fluctuation of Id–Vg curve within seconds
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