Prediction of Process Variation Effect for Ultrascaled GAA Vertical FET Devices Using a Machine Learning Approach

Ko, Kyoungtae; Lee, Jang Kyu; Kang, Min-Ho; Jeon, Jongwook; Shin, Hyungcheol

doi:10.1109/ted.2019.2937786

Cited by 51 publications

(24 citation statements)

References 13 publications

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“…These ML frameworks are entirely based on device data and do not include device physics. In addition to this, the ML techniques have been further used to predict and model the characteristics variations that occurred in different semiconductor devices due to WKF [49], random dopant distribution (RDD) [50], line-edge-roughness (LER) [51], [52], process variation effect (PVE) [53], etc. 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.…”

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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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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“…Nevertheless, they have highlighted their approach still has a high ER due to many preconditions. Thus, they proposed an artificialneural-network-based machine learning (ML) approach with more accurate results for process variations [25]. Recently, various deep learning (DL) techniques have been studied for the WKF on the GAA Si NW devices with high efficiency and accuracy [26].…”

Section: Table I the Process Parametersmentioning

confidence: 99%

A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

Sung

Li²

2021

IEEE Access

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To estimate characteristic fluctuation of emerging devices, three-dimensional device simulation has been performed intensively for various random cases; however, it strongly relies on huge computational resource. In this paper, we report a nanosized-metal-grain pattern-dependent model by using the method of multi-variable non-linear regression (MVNLR) for the threshold voltage fluctuation (Vth) of gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors. The model is developed by perturbing the local metal grains of specific patterns and summing up each metal grain of various grain patterns. The method of MVNLR is applied to build equations for nominal devices, and devices with high work-function (WK) and low WK with respective to process parameters (radius of channel, gate length, channel doping, and oxide thickness), which are generated by the design of experiments. The proposed model can estimate the magnitude of Vth accurately (error rate < 1%) for all parameters, where an error-correction process is further adopted by using a simplified one-perceptron method.

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“…Being one of the cost effective and viable approach to explore the various aspects of the device designs and performance which can assist the fabrication goals by providing approximate estimations of critical device and physical parameters. Moreover, it is one of the best alternatives for data generation when working on ML based problems 24–26 …”

Section: Introductionmentioning

confidence: 99%

“…Moreover, it is one of the best alternatives for data generation when working on ML based problems. [24][25][26] The simulation's speed, convergence, and accuracy are governed by the compact model of the device used to describe the device's physics and various performance parameters. There exist several models for AlGaN/GaN HEMTs such as charge based analytical models, surface voltage based empirical models, and physics based compact models.…”

mentioning

confidence: 99%

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

Mishra

Raut

Sehra

et al. 2022

Int J RF Mic Comp-Aid Eng

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Prediction of Process Variation Effect for Ultrascaled GAA Vertical FET Devices Using a Machine Learning Approach

Cited by 51 publications

References 13 publications

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

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

A Nanosized-Metal-Grain Pattern-Dependent Threshold Voltage Model for the Work Function Fluctuation of GAA Si NW MOSFETs

Multilayer perceptron–random forest based hybrid machine learning–neural network model for GaN high electron mobility transistor's parameter estimations

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