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

Butola, Rajat; Li, Yiming; Kola, Sekhar Reddy

doi:10.1109/access.2022.3188690

Cited by 18 publications

(9 citation statements)

References 63 publications

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“…In recent years, deep learning (DL) [11] and machine learning (ML) [12] have made breakthroughs in image recognition, machine translation, decision-making, and nanostructure design and have relevant applications in GAAFET research. RAJAT BUTOLA et al proposed a method for modeling intrinsic parameter fluctuation of GAA silicon nanosheet MOSFETs using ML [13]. Akbar et al used ML methods to assist device simulation of work function fluctuations for 3D multi-channel gates around silicon nanosheet MOSFETs [14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of electrical properties of GAAFET based on integrated learning model

Zhang,

Chen,

Wang

et al. 2024

Nanotechnology

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As device feature sizes continue to decrease and fin field effect transistors (FinFETs) reach their physical limits, gate all around field effect transistors (GAAFETs) have emerged with larger gate control areas and stackable characteristics for better suppression of second-order effects such as short-channel effects due to their gate encircling characteristics. Traditional methods for studying the electrical characteristics of devices are mostly based on the technology computer-aided design (TCAD). Still, it is not conducive to developing new devices due to its time-consuming and inefficient drawbacks. Deep learning (DL) and machine learning (ML) have been well-used in recent years in many fields. In this paper, we propose an integrated learning model that integrates the advantages of DL and ML to solve many problems in traditional methods. This integrated learning model predicts the direct current characteristics, capacitance characteristics, and electrical parameters of GAAFET better than those predicted by DL or ML methods alone, with a linear regression factor (R2) greater than 0.99 and very small root mean square error (RMSE). The proposed integrated learning model achieves fast and accurate prediction of GAAFET electrical characteristics, which provides a new idea for device and circuit simulation and characteristics prediction in microelectronics.

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Section: Introductionmentioning

confidence: 99%

Prediction of electrical properties of GAAFET based on integrated learning model

Zhang,

Chen,

Wang

et al. 2024

Nanotechnology

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“…In various simulation fields governed by partial differential equations (PDEs), such as TCAD, computational fluid dynamics, and structural analysis, methodologies to model the relationship between the simulation parameters and its output using machine learning rather than using conventional solvers based on the difference method are being actively investigated. [6][7][8][9][10][11][12][13][14][15] For example, for the simulation of semiconductors, neural networks (NNs) have been used to predict device characteristics of metaloxide-semiconductor field-effect transistors (MOSFETs) [16,17] and Gaussian process regression has been used for GaN lightemitting diode structure optimization. [18] The annealing process for recovering crystals containing point defects introduced by ion implantation has a significant impact on the device properties.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Semiconductor Process Simulation Described by Coupled Partial Differential Equations

Sato

Kutsukake

Harada

et al. 2023

Advcd Theory and Sims

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Technology computer‐aided design (TCAD) simulation is an important tool for the development of semiconductor devices. Based on coupled partial differential equations (PDEs) for behaviors, TCAD can calculate objects such as impurities, point defects, and electronic carriers in semiconductors. However, over recent years semiconductor devices have become increasingly miniaturized and complicated, resulting in much longer calculation times for TCAD. Machine learning is one technology that may be used to overcome this simulation cost problem. In this study, a neural network architecture is proposed that considers the structure of the coupled PDEs. Features representing each concentration distribution of the calculation objects are extracted by convolution operations and their reaction is modeled by channel attention. The performance of the proposed architecture and of conventional neural network models is evaluated using a simulation dataset generated by 1D coupled PDEs that models the diffusion and reaction of vacancies and interstitial atoms. In addition, the advantage of the method is discussed through the analyses of error correlations of the two predictions and attention coefficients. The machine learning method developed in this study will be applicable to other physics described by coupled PDEs and is expected to speed up the computation of simulations in various fields.

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“…At circuit level, ML techniques have been applied to predict the current-voltage curves needed for NW FETs compact models [ 11 ]. At device level, several works have analyzed the impact of MGG or/and RDD induced variability in GAA NW FETs [ 12 , 13 ] and NS FETs [ 14 , 15 ]. However, other sources of variability, such as LER, have not been investigated so far.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

García‐Loureiro¹,

Seoane²,

Fernández³

et al. 2023

PLoS ONE

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The performance and reliability of semiconductor devices scaled down to the sub-nanometer regime are being seriously affected by process-induced variability. To properly assess the impact of the different sources of fluctuations, such as line edge roughness (LER), statistical analyses involving large samples of device configurations are needed. The computational cost of such studies can be very high if 3D advanced simulation tools (TCAD) that include quantum effects are used. In this work, we present a machine learning approach to model the impact of LER on two gate-all-around nanowire FETs that is able to dramatically decrease the computational effort, thus reducing the carbon footprint of the study, while obtaining great accuracy. Finally, we demonstrate that transfer learning techniques can decrease the computing cost even further, being the carbon footprint of the study just 0.18 g of CO2 (whereas a single device TCAD study can produce up to 2.6 kg of CO2), while obtaining coefficient of determination values larger than 0.985 when using only a 10% of the input samples.

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

Cited by 18 publications

References 63 publications

Prediction of electrical properties of GAAFET based on integrated learning model

Prediction of electrical properties of GAAFET based on integrated learning model

Machine Learning for Semiconductor Process Simulation Described by Coupled Partial Differential Equations

A machine learning approach to model the impact of line edge roughness on gate-all-around nanowire FETs while reducing the carbon footprint

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