Machine Learning Aided Device Simulation of Work Function Fluctuation for Multichannel Gate-All-Around Silicon Nanosheet MOSFETs

In order to strengthen the supervision and management of environmental pollution and timely understand and record the basic information of potential environmental pollution of enterprises and institutions, this paper proposes a general survey and inventory method of pollution sources based on big data technology under machine learning. Firstly, this paper evaluates and screens the data provided by government departments, constructs a machine learning classification model, and uses a variety of classification algorithms to compare and analyze according to the basic idea of machine learning to deal with practical problems. Then, the calibration data set constructed is used as the training set to predict and classify the national industrial and commercial data, provincial industrial and commercial data, and municipal industrial and commercial data provided by government departments. The experimental results show that the naive Bayesian classification algorithm is the best algorithm, and the F1 values of each data set are increased by 32.92%, 21.42%, and 14.91%, respectively. The classified prediction of the screened Internet data shows that the accuracy of the final Internet supplementary data is 17.26%, which is similar to the industrial and commercial data of the city. The availability of the machine learning model established in this paper is proven.

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“…Suppose that for the word t i in a document d i , the word frequency of t i can be expressed as follows [15]:…”

Section: Methodsmentioning

confidence: 99%

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Liu

Sun

Jing

et al. 2022

Wireless Communications and Mobile Computing

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“…In some work, ML is also applied for the estimation of threshold voltage variability by random telegraph noise fluctuation [54], and by device parameter variability [55], etc. In some of our previous work, the WKF fluctuations are modeled using ML for nanosheet devices such as, in [56], the ML technique is proposed to suppress the WKF effect on characteristics of 3-channel NS devices by using a random forest regressor-based ML model. In [57], ML is utilized to identify the WKF patterns on the metal gate to reduce the impact on DC characteristics of the GAA NSFETs.…”

Section: A Preliminaries and Related Workmentioning

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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“…In the WoDE situational analysis, the machine trained using TCAD data often exhibits over-fitting issues that could potentially be addressed using principal component analysis (PCA) [12], noise [18], or autoencoder [16]. The ML is effectively utilised in predicting the different semiconductor device characteristics variations generated by various sources of fluctuation, such as the geometric-variation effect [19], work function fluctuation [20], the impact of interface trap charges and random dopant fluctuations [21]. With excellent precision, ML is utilised to predict the I-V and C-V curves of FinFETs using their geometrical properties.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor

Kumar,

Rajakumari,

Padhy

et al. 2024

Phys. Scr.

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In this article, the possibilities of accurate prediction of wide range of parameters and optimizing the same through machine learning (ML) approach have been demonstrated for the multi stacked nanosheet transistor (NSFET). The machine is trained by the generated data of the tedious calibrated technology computer aided (TCAD) simulations. An innovative strategy is employed that combines ML with device simulations. Numerous devices are simulated with different geometric parameters like height, width, length and equivalent oxide thickness of the channel. The input, output, and CV characteristics are extrapolated from the simulation which is predicted by ML models. The DC, Analog and RF parameters are derived with a domain expertise approach. The device parameters are anticipated with actual values by the ML approach. Random forest regression, linear regression, polynomial regression and decision tree regression are employed for the prediction of the performance parameters. Random forest regression models provide significant R\textsuperscript{2} score with minimal error percentage. It indicates that TCAD-augmented ML can be considered an alternative to device simulation due to its reduction in computational cost. This work can also be treated as a benchmark for accurate prediction of the NSFET.

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Machine Learning Aided Device Simulation of Work Function Fluctuation for Multichannel Gate-All-Around Silicon Nanosheet MOSFETs

Cited by 30 publications

References 28 publications

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

Census and Inventory Method of Pollution Sources Based on Big Data Technology under Machine Learning

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

A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor

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