Statistical Prediction of Nanosized-Metal-Grain-Induced Threshold-Voltage Variability for 3D Vertically Stacked Silicon Gate-All-Around Nanowire n-MOSFETs

Sung, Wen-Li; Li, Yiming

doi:10.1007/s11664-020-08332-2

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…Furthermore, the absolute value of ER is lower than 0.4%. Thus, the (13) can be used to fit the data for the GAA devices with multi-channels [14].…”

Section: Resultsmentioning

confidence: 99%

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A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

Sung

Li²

2021

IEEE Access

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To estimate characteristic fluctuation of emerging devices, three-dimensional device simulation has been performed intensively for various random cases; however, it strongly relies on huge computational resource. In this paper, we report a nanosized-metal-grain pattern-dependent model by using the method of multi-variable non-linear regression (MVNLR) for the threshold voltage fluctuation (Vth) of gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors. The model is developed by perturbing the local metal grains of specific patterns and summing up each metal grain of various grain patterns. The method of MVNLR is applied to build equations for nominal devices, and devices with high work-function (WK) and low WK with respective to process parameters (radius of channel, gate length, channel doping, and oxide thickness), which are generated by the design of experiments. The proposed model can estimate the magnitude of Vth accurately (error rate < 1%) for all parameters, where an error-correction process is further adopted by using a simplified one-perceptron method.

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“…Furthermore, the absolute value of ER is lower than 0.4%. Thus, the (13) can be used to fit the data for the GAA devices with multi-channels [14].…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, the GAA devices with multi-channels have been considered to increase the drive current [5]. The estimation of variability of the GAA devices requires huge computational resource based on the 3D-DS [14].…”

Section: Introductionmentioning

confidence: 99%

A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

Sung

Li²

2021

IEEE Access

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Fernández

Seoane

Comesaña

et al. 2022

IEEE J. Electron Devices Soc.

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The impact of different variability sources on the transistor performance increases as devices are scaled-down, being the metal grain granularity (MGG) and the line edge roughness (LER) some of the major contributors to this increase. Variability studies require the simulation of large samples of different device configurations to have statistical significance, increasing the computational cost. A novel Pelgrom-based predictive (PBP) model that estimates the impact of MGG and LER through the study of the threshold voltage standard deviation (σ V Th ), is proposed. This technique is computationally efficient since once the threshold voltage mismatch is calculated, σ V Th can be predicted for different gate lengths (L g ), cross-sections, and intrinsic variability parameters, without further simulations. The validity of the PBP model is demonstrated for three state-of-the-art architectures (FinFETs, nanowire FETs, and nanosheet FETs) with different L g , cross-sections, and drain biases (V D ). The relative errors between the predicted and simulated data are lower than 10%, in the 92% of the cases.

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Nanosized-Metal-Grain-Pattern-Dependent Threshold-Voltage Models for the Vertically Stacked Multichannel Gate-All-Around Si Nanosheet MOSFETs and Their Applications in Circuit Simulation

Sung,

Li,

Chuang

2024

IEEE Trans. Electron Devices

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Statistical Prediction of Nanosized-Metal-Grain-Induced Threshold-Voltage Variability for 3D Vertically Stacked Silicon Gate-All-Around Nanowire n-MOSFETs

Cited by 3 publications

References 21 publications

A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

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

Nanosized-Metal-Grain-Pattern-Dependent Threshold-Voltage Models for the Vertically Stacked Multichannel Gate-All-Around Si Nanosheet MOSFETs and Their Applications in Circuit Simulation

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