Deep Learning Approach to Estimating Work Function Fluctuation of Gate-All-Around Silicon Nanosheet MOSFETs with A Ferroelectric HZO Layer

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

doi:10.1109/edtm53872.2022.9798172

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Akbar et al used ML methods to assist device simulation of work function fluctuations for 3D multi-channel gates around silicon nanosheet MOSFETs [14]. DL algorithms are also used to evaluate the work function fluctuations of GAAFETs [15,16].…”

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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“…Computational studies, such as the Fourier-Bessel model [16], machine learning (ML) methodologies [17], and deep learning (DL) [18] approaches are much needed in the WKF-induced device variability. Due to their reliability and scalability while upholding the same efficiency, ML/DL approaches are playing crucial roles in applied science and technology, such as image classification [19], computer vision [20], machine translation [21], human-computer interaction [22], natural language processing [23], and many more.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

Dash

Sung

2023

IEEE Access

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Presently deep learning (DL) techniques are massively used in the semiconductor industry. At the same time, applying a deep learning approach for small datasets is also an immense challenge as larger dataset generation needs more computational time-cost factors for technology computer-aided design (TCAD) simulation. In this paper, to overcome the aforesaid issue, a hybrid DL-aided prediction of electrical characteristics of the multichannel devices induced by work function fluctuation (WKF) with a smaller dataset is proposed. For the first time, an amalgamation approach of two deep learning algorithms (i.e.1D-CNN and LSTM) is implemented for all four channels (1 to 4 channels) of gate-all-around (GAA) silicon Nanosheet and Nanofin MOSFETs (NS-FETs and NF-FETs). The proposed joint learning framework combines a one-dimensional convolutional neural network (1D-CNN) with long short-term memory (LSTM) model. In this architecture, CNN can extract the features efficiently from the input WKF, and LSTM identifies the historical sequence of the captured features of the input regression data. To illustrate the excellency of the proposed approach, a comparative study of our hybrid model along with three individual DL models i.e. 1D-CNN and LSTM including a baseline multilevel perceptron (MLP) model are demonstrated for a promising small dataset (i.e.1100 samples). The results indicate a superior prediction of 1D-CNN-LSTM in terms of root mean square error (RMSE) with an average value of 1.7943 X10 −7 and R 2 Score with an average value of 96.18% within the shortest time span in contrast to the other three algorithms. Finally, it can be quantified according to the evaluation and performance that hybrid methodology not only adopts the complexity of both NS-and NF-FETs, but also estimates the characteristics of all four channels of it efficiently with a smaller dataset, lesser time span, and reduced computational cost.

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Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

Akbar

Thoti

2022

IEEE Access

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Deep Learning Approach to Estimating Work Function Fluctuation of Gate-All-Around Silicon Nanosheet MOSFETs with A Ferroelectric HZO Layer

Cited by 3 publications

References 4 publications

Prediction of electrical properties of GAAFET based on integrated learning model

Prediction of electrical properties of GAAFET based on integrated learning model

A Hybrid 1D-CNN-LSTM Technique for WKF-Induced Variability of Multi-Channel GAA NS- and NF-FETs

Device-Simulation-Based Machine Learning Technique for the Characteristic of Line Tunnel Field-Effect Transistors

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