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

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 455
Author(s):  
Jinwoong Lee ◽  
Taeeon Park ◽  
Hongjoon Ahn ◽  
Jihwan Kwak ◽  
Taesup Moon ◽  
...  
Keyword(s):  
Prediction Model ◽  
Threshold Voltage ◽  
Drain Current ◽  
Ann Model ◽  
Line Edge Roughness ◽  
Threshold Voltages ◽  
Edge Roughness ◽  
Artificial Neural Network Ann ◽  
The Impact ◽  
Random Variation

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.

Impact of interface trap charges on Junctionless double and triple metal gate High-k Gate All Around Nanowire FET based Alzheimer Biosensor

2021 ◽  
Author(s):  
Rishu Chaujar ◽  
Mekonnen Getnet Yirak
Keyword(s):  
Threshold Voltage ◽  
Positive Impact ◽  
Drain Current ◽  
Interface Trap ◽  
Metal Gate ◽  
High K ◽  
Current Threshold ◽  
Subthreshold Voltage ◽  
Output Characteristics ◽  
The Impact

Abstract In this work, junctionless double and triple metal gate high-k gate all around nanowire field-effect transistor-based APTES biosensor has been developed to study the impact of ITCs on device sensitivity. The analytical results were authenticated using ‘‘ATLAS-3D’’ device simulation tool. Effect of different interface trap charge on the output characteristics of double and triple metal gate high-k gate all around junctionless NWFET biosensor was studied. Output characteristics, like transconductance, output conductance,drain current, threshold voltage, subthreshold voltage and switching ratio, including APTES biomolecule, have been studied in both devices. 184% improvement has been investigated in shifting threshold voltage in a triple metal gate compared to a double metal gate when APTES biomolecule immobilizes on the nanogap cavity region under negative ITCs. Based on this finding, drain off-current ratio and shifting threshold voltage were considered as sensing metrics when APTES biomolecule immobilizes in the nanogap cavity under negative ITCs which is significant for Alzheimer's disease detection. We signifies a negative ITC has a positive impact on our proposed biosensor device compared to positive and neutral ITCs.

Line edge roughness induced threshold voltage variability in nano-scale FinFETs

2017 ◽  
Vol 103 ◽  
pp. 304-313 ◽  
Author(s):  
Rituraj Singh Rathore ◽  
Rajneesh Sharma ◽  
Ashwani K. Rana
Keyword(s):  
Threshold Voltage ◽  
Line Edge Roughness ◽  
Nano Scale ◽  
Edge Roughness

scholarly journals Line-Edge Roughness from Extreme Ultraviolet Lithography to Fin-Field-Effect-Transistor: Computational Study

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1493
Author(s):  
Sang-Kon Kim
Keyword(s):  
Field Effect ◽  
Extreme Ultraviolet ◽  
Field Effect Transistors ◽  
Computational Study ◽  
Drain Current ◽  
Extreme Ultraviolet Lithography ◽  
Line Edge Roughness ◽  
Stochastic Fluctuation ◽  
Ultraviolet Lithography ◽  
Edge Roughness

Although extreme ultraviolet lithography (EUVL) has potential to enable 5-nm half-pitch resolution in semiconductor manufacturing, it faces a number of persistent challenges. Line-edge roughness (LER) is one of critical issues that significantly affect critical dimension (CD) and device performance because LER does not scale along with feature size. For LER creation and impacts, better understanding of EUVL process mechanism and LER impacts on fin-field-effect-transistors (FinFETs) performance is important for the development of new resist materials and transistor structure. In this paper, for causes of LER, a modeling of EUVL processes with 5-nm pattern performance was introduced using Monte Carlo method by describing the stochastic fluctuation of exposure due to photon-shot noise and resist blur. LER impacts on FinFET performance were investigated using a compact device method. Electric potential and drain current with fin-width roughness (FWR) based on LER and line-width roughness (LWR) were fluctuated regularly and quantized as performance degradation of FinFETs.

scholarly journals Impact of biomass burning emission on total peroxy nitrates: fire plume identification during the BORTAS campaign

2016 ◽  
Vol 9 (11) ◽  
pp. 5591-5606 ◽  
Author(s):  
Eleonora Aruffo ◽  
Fabio Biancofiore ◽  
Piero Di Carlo ◽  
Marcella Busilacchio ◽  
Marco Verdecchia ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Atmospheric Pressure ◽  
Thermal Dissociation ◽  
Ann Model ◽  
Fire Plume ◽  
Pressure Threshold ◽  
Peroxy Nitrates ◽  
Artificial Neural Network Ann ◽  
The Impact

Abstract. Total peroxy nitrate ( ∑ PN) concentrations have been measured using a thermal dissociation laser-induced fluorescence (TD-LIF) instrument during the BORTAS campaign, which focused on the impact of boreal biomass burning (BB) emissions on air quality in the Northern Hemisphere. The strong correlation observed between the  ∑ PN concentrations and those of carbon monoxide (CO), a well-known pyrogenic tracer, suggests the possible use of the  ∑ PN concentrations as marker of the BB plumes. Two methods for the identification of BB plumes have been applied: (1)  ∑ PN concentrations higher than 6 times the standard deviation above the background and (2)  ∑ PN concentrations higher than the 99th percentile of the  ∑ PNs measured during a background flight (B625); then we compared the percentage of BB plume selected using these methods with the percentage evaluated, applying the approaches usually used in literature. Moreover, adding the pressure threshold ( ∼  750 hPa) as ancillary parameter to  ∑ PNs, hydrogen cyanide (HCN) and CO, the BB plume identification is improved. A recurrent artificial neural network (ANN) model was adapted to simulate the concentrations of  ∑ PNs and HCN, including nitrogen oxide (NO), acetonitrile (CH3CN), CO, ozone (O3) and atmospheric pressure as input parameters, to verify the specific role of these input data to better identify BB plumes.

An ANN Model to Estimate the Impact of Tea Process Parameters on Tea Quality

2015 ◽  
Vol 24 (09) ◽  
pp. 1550139
Author(s):  
Debashis Saikia ◽  
Diganta Kumar Sarma ◽  
P. K. Boruah ◽  
Utpal Sarma
Keyword(s):  
Process Condition ◽  
Correlation Study ◽  
Sensor Nodes ◽  
Quality Data ◽  
Inlet Temperature ◽  
Ann Model ◽  
Classification Rate ◽  
Tea Quality ◽  
Artificial Neural Network Ann ◽  
The Impact

Present study deals with the development of an artificial neural network (ANN)-based technique for tea quality quantification by monitoring fermentation and drying condition of the tea processing stages. An RS485 network-based instrumentation system has been developed and implemented for data collection for these two stages. Three calibrated sensor nodes are installed in the fermentation room due to its larger floor area to collect temperature and relative humidity (RH). Dryer inlet temperature is recorded using a calibrated thermocouple-based sensor node. From seven input parameters and target quality data obtained from tea taster, the ANN model has been developed to find the correlation between the process condition and the tea quality. From the correlation study, more than 90% classification rate is obtained from the model. The model is also validated with some independent data showing more than 60% correlation. Error in terms of root mean square error (RMSE) is about 0.17. This model will be helpful for improvement of tea quality.

scholarly journals Advanced approaches to hot-mix asphalt dynamic modulus prediction

2008 ◽  
Vol 35 (7) ◽  
pp. 699-707 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Logical Structure ◽  
Ann Model ◽  
Model Predictions ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

scholarly journals Characteristic diffuse scattering from distinct line roughnesses

2017 ◽  
Vol 50 (6) ◽  
pp. 1766-1772 ◽  
Author(s):  
Analía Fernández Herrero ◽  
Mika Pflüger ◽  
Jürgen Probst ◽  
Frank Scholze ◽  
Victor Soltwisch
Keyword(s):  
Extreme Ultraviolet ◽  
Structural Elements ◽  
Line Edge Roughness ◽  
Optical Elements ◽  
Angle Scattering ◽  
Edge Roughness ◽  
Angular Scattering ◽  
The Impact

Lamellar gratings are widely used diffractive optical elements; gratings etched into Si can be used as structural elements or prototypes of structural elements in integrated electronic circuits. For the control of the lithographic manufacturing process, a rapid in-line characterization of nanostructures is indispensable. Numerous studies on the determination of regular geometry parameters of lamellar gratings from optical and extreme ultraviolet (EUV) scattering highlight the impact of roughness on the optical performance as well as on the reconstruction of these structures. Thus, a set of nine lamellar Si gratings with a well defined line edge roughness or line width roughness were designed. The investigation of these structures using EUV small-angle scattering reveals a strong correlation between the type of line roughness and the angular scattering distribution. These distinct scattering patterns open new paths for the unequivocal characterization of such structures by EUV scatterometry.

scholarly journals Antioxidant Activity of Selected Polyphenolics in Yeast Cells: The Case Study of Montenegrin Merlot Wine

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1971 ◽  
Author(s):  
Neda Đorđević ◽  
Nevena Todorović ◽  
Irena Novaković ◽  
Lato Pezo ◽  
Boris Pejin ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Defense System ◽  
Yeast Cells ◽  
Total Phenolic ◽  
Ann Model ◽  
Phenolic Components ◽  
Stressed Cells ◽  
Pre Treatment ◽  
Artificial Neural Network Ann ◽  
The Impact

Screens of antioxidant activity (AA) of various natural products have been a focus of the research community worldwide. This work aimed to differentiate selected samples of Merlot wines originated from Montenegro, with regard to phenolic profile and antioxidant capacity studied by survival rate, total sulfhydryl groups and activities of glutathione peroxidase (GPx), glutathione reductase and catalase in H2O2–stressed Saccharomyces cerevisiae cells. In this study, DPPH assay was also performed. Higher total phenolic content leads to an enhanced AA under both conditions. The same trend was observed for catechin and gallic acid, the most abundant phenolics in the examined wine samples. Finally, the findings of an Artificial Neural Network (ANN) model were in a good agreement (r2 = 0.978) with the experimental data. All tested samples exhibited a protective effect in H2O2–stressed yeast cells. Pre-treatment with examined wines increased survival in H2O2–stressed cells and shifted antioxidative defense towards GPx–mediated defense. Finally, sensitivity analysis of obtained ANN model highlights the complexity of the impact that variations in the concentrations of specific phenolic components have on the antioxidant defense system.

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