Work-Function Fluctuation of Gate-All-Around Silicon Nanowire n-MOSFETs: A Unified Comparison Between Cuboid and Voronoi Methods

Sung, Wen-Li; Yang, Ya-Shu; Li, Yiming

doi:10.1109/jeds.2020.3046608

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…The adopted HZO parameters are listed in Table 3 , where E is the electric field, P is the polarization, α , β and γ are FE parameters which have been calibrated with the experimental data [ 29 ]. Moreover, the simulation technique of WKF mainly follows our recent work in [ 30 ]. The cuboid method can provide a robust way to predict and analyze the degree of variability accurately compared with the Voronoi method.…”

Section: Methodsmentioning

confidence: 99%

Effects of Random Nanosized TiN Grain on Characteristic of Gate-All-Around FinFETs with Ferroelectric HZO Layer

Chuang

Tsai

2022

Nanoscale Res Lett

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In this paper, we computationally study electrical characteristics for gate-all-around fin field effect transistors (GAA FinFETs) and negative capacitance GAA FinFETs (NC-GAA FinFETs) for sub-3-nm technological nodes. For the devices with the fin height of 55 nm, the on-state current increases (about 33% improvement) and the off-state current decreases (about 73% suppression) due to the NC effect. NC-GAA FinFETs have larger standard deviation of threshold voltage induced by the workfunction fluctuation (WKF) for both N-/P-type devices than those of GAA FinFETs. It is attributed to the variation of polarization in the different position of the ferroelectric layer. Notably, the inverter of NC-GAA FinFETs has larger noise margin and shorter delay time, compared with the inverter of GAA FinFETs; however, the characteristics of inverter of NC-GAA FinFETs suffer larger variability induced by the WKF.

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Section: Methodsmentioning

confidence: 99%

Effects of Random Nanosized TiN Grain on Characteristic of Gate-All-Around FinFETs with Ferroelectric HZO Layer

Chuang

Tsai

2022

Nanoscale Res Lett

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“…The titanium nitride (TiN) layer is used as the metal gate for the device and it is composed of metal grains (MG) of sizes 3.92 nm × 3 nm. For all WKF fluctuated devices, the random patterns of low/high work function (WK) are generated by using the Monte Carlo method [14]. The low WK is referred to as TiN<111> and high WK is referred to as TiN<200> with the probability of occurrence as 40% and 60% with WK = 4.4 eV and WK = 4.6 eV, respectively as shown in Fig.…”

Section: Device Simulation Fluctuation Sources and Data Generationmentioning

confidence: 99%

“…It also provides more conducting channels, improved power performance, and better area scaling compared to its other competitor transistors such as FinFET and nanowire [8][9][10][11]. However, nanoscale devices are suffered from different kinds of fluctuations that cause variability in their electrical characteristics [12][13][14][15] and the miniaturization increases the device sensitivity to microscopic perturbation. Therefore, even a small fluctuation may become a critical issue for device characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

Butola

Kola

2022

IEEE Access

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The sensitivity of semiconductor devices to any microscopic perturbation is increasing with the continuous shrinking of device technology. Even the small fluctuations have become more acute for highly scaled nano-devices. Therefore, these fluctuations need to be addressed extensively in order to continue further device scaling. In this paper, we mainly focus on three intrinsic parameter fluctuation sources, work function fluctuation (WKF), random dopant fluctuation (RDF), and interface trap fluctuation (ITF) for gate-all-around (GAA) silicon nanosheet (NS) MOSFETs. Generally, the effect of these fluctuations is analyzed using a time-consuming device simulation process. A machine learning (ML) based powerful and efficient artificial neural network (ANN) model is used to accelerate this process. Firstly, the effects of fluctuation sources are analyzed individually by using the ANN model and results have been presented that show the WKF variations dominate the threshold voltage (VTH), off-state current (IOFF), and on-state current (ION) variations among other fluctuation sources. Next, we examine the combined effect of all three fluctuation sources. It is crucial because considering only one fluctuation can result in unexpected variations due to other fluctuations present in the device. Therefore, the ANN model is used to estimate the combined effects as well. The results show that the proposed model predicts the outputs with an R 2 -score of 99% and an error rate of less than 1%. In addition, the ML is also utilized to calculate the permutation importance of input variables as a measure to investigate the contribution of each fluctuation source.

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“…Due to the (2) will be fed into the ( 3) and ( 4), the (2) must be calculated before the separation of the HWK and LWK. After the separation of the HWK and LWK, the (3) or (4) will be calculated and then the perturbation of each metal grain with HWK and LWK can be obtained by the ( 9) or (10), respectively. After executing the loop of k from 1 to MGN, the sum of the perturbations of Vth will be obtained by the (11).…”

Section: C-2 the Overall Equations And Their Computational Flowmentioning

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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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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Work-Function Fluctuation of Gate-All-Around Silicon Nanowire n-MOSFETs: A Unified Comparison Between Cuboid and Voronoi Methods

Cited by 15 publications

References 21 publications

Effects of Random Nanosized TiN Grain on Characteristic of Gate-All-Around FinFETs with Ferroelectric HZO Layer

Effects of Random Nanosized TiN Grain on Characteristic of Gate-All-Around FinFETs with Ferroelectric HZO Layer

A Machine Learning Approach to Modeling Intrinsic Parameter Fluctuation of Gate-All-Around Si Nanosheet MOSFETs

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

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