Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Chen, Yin Nien; Chen, Chien Ju; Fan, Ming-Long; Hu, Vita Pi‐Ho; Su, Pin; Chuang, Ching Te

doi:10.3390/jlpea5020101

Cited by 10 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A kind of Verilog‐A look‐up table model can be adopted for a SPICE simulation of the device loop and the circuit loop. [ 196,197 ] A neural network model can also be used for novel devices based on 2D semiconductor materials. [ 198,199 ] The processes of fabrication of 2D semiconductor FETs have a great influence on the performance of the device, and one that was reported based on the mechanical exfoliation method exhibited perfect performance.…”

Section: Model and Simulation Of 2d Semiconductor Devices And Vlsimentioning

confidence: 99%

The Trend of 2D Transistors toward Integrated Circuits: Scaling Down and New Mechanisms

et al. 2022

View full text Add to dashboard Cite

2D transition metal chalcogenide (TMDC) materials, such as MoS2, have recently attracted considerable research interest in the context of their use in ultrascaled devices owing to their excellent electronic properties. Microprocessors and neural network circuits based on MoS2 have been developed at a large scale but still do not have an advantage over silicon in terms of their integrated density. In this study, the current structures, contact engineering, and doping methods for 2D TMDC materials for the scaling‐down process and performance optimization are reviewed. Devices are introduced according to a new mechanism to provide the comprehensive prospects for the use of MoS2 beyond the traditional complementary–metal–oxide semiconductor in order to summarize obstacles to the goal of developing high‐density and low‐power integrated circuits (ICs). Finally, prospects for the use of MoS2 in large‐scale ICs from the perspectives of the material, system performance, and application to nonlogic functionalities such as sensor circuits and analogous circuits, are briefly analyzed. The latter issue is along the direction of “more than Moore” research.

show abstract

Section: Model and Simulation Of 2d Semiconductor Devices And Vlsimentioning

confidence: 99%

The Trend of 2D Transistors toward Integrated Circuits: Scaling Down and New Mechanisms

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Two ANN models consist of four hidden layers. "ReLU" was used as the active function [18]. The "batch normalization" [19] was applied to each hidden layer, and 20% of the training sets were used as the validation data sets to prevent overfitting issues.…”

Section: Simulationmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

“…In this study, we assumed that the distribution of performance metrics follows the multi-variate Gaussian distribution to securely build the model for estimating the LER-induced performance variation of device. It is known that the LER-induced variation of Vt, Ion, SS, and log10Ioff approximately follows the Gaussian distribution in various devices [11], [21], [22].…”

Section: B Grafting Probability Distributionmentioning

confidence: 99%

Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

Lim

Shin

2020

IEEE Access

View full text Add to dashboard Cite

ML (Machine Learning)-based artificial neural network (ANN) model is proposed to estimate the LER (line edge roughness)-induced performance variation in Fin-shaped Field Effect Transistor (FinFET). For a given LER features such as rms amplitude(Δ), correlation length along x-axis (ΛX), and correlation length along y-axis (ΛY), the metrics for device performance such as on-state drive current, off-state leakage current, threshold voltage, and subthreshold swing can be computing-efficiently estimated with the ANN model. INDEX TERMS Line edge roughness, process-induced random variation, FinFET, Machine Learning, Artificial neural network. Jaehyuk Lim, Changhwan Shin:ML-based model to characterize the LER induced random variation in FinFET Jaehyuk Lim, Changhwan Shin:ML-based model to characterize the LER induced random variation in FinFET VOLUME XX, 2020 9

show abstract

Impacts of Work Function Variation and Line-Edge Roughness on TFET and FinFET Devices and 32-Bit CLA Circuits

Cited by 10 publications

References 14 publications

The Trend of 2D Transistors toward Integrated Circuits: Scaling Down and New Mechanisms

The Trend of 2D Transistors toward Integrated Circuits: Scaling Down and New Mechanisms

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

Machine Learning (ML)-Based Model to Characterize the Line Edge Roughness (LER)-Induced Random Variation in FinFET

Contact Info

Product

Resources

About