Modelling the spice parameters of SOI MOSFET using a combinational algorithm

Sarvaghad-Moghaddam, Moein; Orouji, Ali A.; Elhoseny, Mohamed; Farouk, Ahmed; Kumar, Naveen

doi:10.1007/s10586-018-2289-6

Cited by 29 publications

(3 citation statements)

References 22 publications

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“…In Panko et al, 18 the impact of several parasitic bipolar devices is considered based on standard MOSFET models (BSIM3/BSIM4). Sarvaghad‐Moghaddam et al 19 and Tang and Li 20 conducted research on the modeling of nanoscale MOSFETs. The modeling of CMOS devices is conducted in Zhang et al, 15 Li et al, 21 and Petrosyants et al 22 Perumal et al 23 present a compact model for flexible analog/RF circuits design with amorphous indium‐gallium‐zinc oxide thin‐film transistors.…”

Section: Introductionmentioning

confidence: 99%

“…Melikian et al 31 present an efficient and reliable method for general semiconductor device parameter extraction and consider the basic principle of SPICE system LEVEL3 MOS transistor model parameters, which is based on iterative specifications. In Sarvaghad‐Moghaddam et al, 19 a combinational algorithm is proposed for modeling a nano‐silicon‐on‐insulator MOSFET characteristic. Yang et al 32 present a datasheet‐driven empirical model of silicon carbide (SiC) MOSFET, in which both the static I – V and dynamic C – V characteristics are captured by using nonsegmented empirical equations.…”

Section: Introductionmentioning

confidence: 99%

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A MOSFET EMC modeling method based on electrical characteristic measurement and simplex optimization and particle swarm optimization

Pan,

Liu,

Ren

et al. 2023

Circuit Theory & Apps

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SummaryMetal oxide semiconductor field effect transistors (MOSFETs) are widely used in various power electronic systems, and the establishment of the electromagnetic compatibility (EMC) model for MOSFETs is crucial for EMC analysis of these systems, especially high‐frequency switching circuits. In this paper, a MOSFET EMC modeling method is proposed based on electrical characteristic measurement, simplex optimization, and particle swarm optimization (PSO), to make MOSFET models meet both functionality and EMC analysis requirements. First, systematic methods for extracting SPICE parameters based on physical MOSFETs are presented, including a proposed curve‐fitting method based on PSO for extracting the CDS‐VDS and ID‐VGS characteristic parameters. Second, parametric influence analyses are conducted on some important model parameters, and the law that these parameters affect the EMC of MOSFETs is obtained. Finally, the preliminary MOSFET model was optimized comprehensively using the simplex method. The measured and simulated results are in good agreement in both time domain and frequency domain. It proves that the established model meets the requirements for model accuracy in terms of both EMC and functionality. Therefore, the proposed EMC modeling method is feasible.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A MOSFET EMC modeling method based on electrical characteristic measurement and simplex optimization and particle swarm optimization

Pan,

Liu,

Ren

et al. 2023

Circuit Theory & Apps

View full text Add to dashboard Cite

show abstract

“…Similarly, a ML algorithm was explored in [32] to yield improvement in semiconductor manufacturing. Nowadays, the integration of ML with the study on GAA Si NW MOSFETs is considered to be significantly feasible and broadly applicable [33][34][35][36][37][38]. The purpose of applying ML is to predict the characteristics of semiconductor devices.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.

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Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and Recommendations

Ismail

Shehab

El-Henawy

2018

Security in Smart Cities: Models, Applications, and Challenges

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Modelling the spice parameters of SOI MOSFET using a combinational algorithm

Cited by 29 publications

References 22 publications

A MOSFET EMC modeling method based on electrical characteristic measurement and simplex optimization and particle swarm optimization

A MOSFET EMC modeling method based on electrical characteristic measurement and simplex optimization and particle swarm optimization

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and Recommendations

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