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

Electronics

Hongjoon Ahn¹  Changhwan Shin²  Jinwoong Lee²  Taeeon Park²  Taesup Moon²  Jihwan Kwak³ 

[1] Department of Artificial Intelligence, Sungkyunkwan University, Suwon 16419, Korea;Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea;Department of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea;

关键词: line-edge roughness (LER);    random variation;    machine learning;    artificial neural network;   

DOI  :  10.3390/electronics10040455

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown