FinFET design considerations based on 3-D simulation and analytical modeling

Pei, Guangsheng; Kedzierski, J.; Oldiges, Phil; Ieong, M.; Kan, Edwin C.

doi:10.1109/ted.2002.801263

Cited by 278 publications

(115 citation statements)

References 23 publications

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“…TCAD simulation are extensively exploited for evaluating the fabrication process and the electrical behavior of semiconductors [12]. During the recent years, TCAD has been used for the simulation of emerging nano-devices such as Multigate FETs [14] and CMOS/Flash devices [13].…”

Section: B Previous Workmentioning

confidence: 99%

A fast TCAD-based methodology for Variation analysis of emerging nano-devices

Mohammadi

Gaillardon

Yazdani

et al. 2013

2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS)

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Abstract-Variability analysis of nanoscale transistors and circuits is emerging as a necessity at advanced technology nodes. Technology Computer Aided Design (TCAD) tools are powerful ways to get an accurate insight of Process Variations (PV). However, obtaining both fast and accurate device simulations is impractical with current TCAD solvers. In this paper, we propose an automated output prediction method suited for fast PV analysis. Coupled with TCAD simulations, our methodology can substantially reduce the time complexity and cost of variation analysis for emerging technologies. We overcome the simulation obstacles and preserve accuracy, using a neural network based regression to predict the output of individual process simulations. Experiments indicate that, after the training process, the proposed methodology effectively accelerate TCAD-based PV simulations close to compact-model-based simulations. Therefore, the methodology can be an excellent opportunity in enabling extensive statistical simulations such as Monte-Carlo for emerging nano-devices.

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Section: B Previous Workmentioning

confidence: 99%

A fast TCAD-based methodology for Variation analysis of emerging nano-devices

Mohammadi

Gaillardon

Yazdani

et al. 2013

2013 IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems (DFTS)

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“…where U (x, y, z) is the surface potential at a particular point (x, y, z), N a (x, y, z) is the uniform channel doping concentration, q is the electronic charge, ε s is the permittivity of silicon, p (x, y, z) is the hole concentration, ∆n is the excess carriers generated per unit volume, ∆Q is the attenuated intensity, n (x, y, z) is the electron concentration [14]. The boundary conditions used for solving Equation (1) is shown in [16].…”

Section: Physics Based Modelingmentioning

confidence: 99%

Numerical Simulation of Nanoscale Finfet Photodetector for Optimal Detection of Biological Signals Using Interpolating Wavelets

Ramesh¹,

Madheswaran²,

Kannan³

2011

PIER B

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Abstract-The biosensor design for sensing of biological signals is highly complex for accurate detection. Optimal detection of biological signals is necessary for distinguishing different tissues. This paper proposes a threshold-based detection technique which provides significant improvement in FinFET optical biosensor performance using wavelet coefficients. It uses a simple maximum likelihood (ML) function for detecting the threshold values. In this method, we have considered the different layers of body tissue as a turbid medium. To the best of our knowledge, this method is the first of its kind for classifying different tissues using threshold value of optical signals obtained from the surface potential variations of nanoscale FinFET illuminated by laser source of different wavelengths. By using this method, the point to point variations in tissue composition and structural variations in healthy and diseased tissues could be identified. The results obtained are used to examine the performance of the device for its suitable use as a nanoscale sensor.

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“…In FinFETs, the shape, dimensions (height and width) and doping level of the original Si fin have importance in carrier transport [2,[8][9]. The shape of fin plays the role how stress from SiGe is exerted from sidewalls to the central part of body.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Selective Growth of SiGe for Source/Drain in 14nm and Beyond Nodes FinFETs

Radamson

Luo

Qin

et al. 2017

Int. J. Hi. Spe. Ele. Syst.

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In this work, optimization of selective epitaxy growth (SEG) of SiGe layers on source/drain (S/D) areas in 14nm node FinFETs with high-k & metal gate has been presented. The Ge content in epilayers was in range of 30%-40% with boron concentration of 1-3 × 10 20 cm −3 . The strain distribution in the transistor structure due to SiGe as stressor material in S/D was simulated and these results were used as feedback to design the layer profile. The epitaxy parameters were optimized to improve the layer quality and strain amount of SiGe layers. The in-situ cleaning of Si fins was crucial to grow high quality layers and a series of experiments were performed in range of 760-825 °C. The results demonstrated that the thermal budget has to be within 780-800 °C in order to remove the native oxide but also to avoid any harm to the shape of Si fins. The Ge content in SiGe layers was directly determined from the misfit parameters obtained from reciprocal space mappings using synchrotron radiation. Atomic layer deposition (ALD) technique was used to deposit HfO 2 as high-k dielectric and B-doped W layer as metal gate to fill the gate trench. This type of ALD metal gate has decent growth rate, low resistivity and excellent capability to fill the gate trench with high aspect-ratio. Finally, the electrical characteristics of fabricated FinFETs were demonstrated and discussed.

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FinFET design considerations based on 3-D simulation and analytical modeling

Cited by 278 publications

References 23 publications

A fast TCAD-based methodology for Variation analysis of emerging nano-devices

A fast TCAD-based methodology for Variation analysis of emerging nano-devices

Numerical Simulation of Nanoscale Finfet Photodetector for Optimal Detection of Biological Signals Using Interpolating Wavelets

Optimization of Selective Growth of SiGe for Source/Drain in 14nm and Beyond Nodes FinFETs

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