Speed Up Quantum Transport Device Simulation on Ferroelectric Tunnel Junction With Machine Learning Methods

Wu, Tong; Guo, Jing

doi:10.1109/ted.2020.3025982

Cited by 19 publications

(13 citation statements)

References 21 publications

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“…In the past few years, it has also been used significantly in the semiconductor industry to solve modeling and optimization problems [27]- [33]. It is successfully employed as a strategy to speed up semiconductor device development and reduce the computational resources [34]- [39]. Various device simulation assisted ML frameworks are reported to solve different problems such as device variation and operating temperature analysis [40], point defect prediction [41], hotspot detection [42], anomaly detection [43], device structural variation identification, and inverse design [44], 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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“…Recently machine learning (ML) and quantum computing (QC) applications are gaining attention in the field of condensed matter physics [23][24][25][26]. Most of the studies so far are focused on the electronic properties [27][28][29] or transport properties [30,31]. The application of ML has significantly reduced the computational requirement as well as time consumption for computationally demanding problems.…”

Section: Introductionmentioning

confidence: 99%

“…The theoretical evaluation of non-equilibrium spin density is done via non-equilibrium Green's function technique [34][35][36] which is computationally computationally quite demanding. Compared to that, prediction with trained learning algorithm is quite efficient [30,31] and allows to study a large number of configuration for a given system. For a given system, the spintronic properties are usually dominated by a subset of parameters necessary to define the whole system.…”

Section: Introductionmentioning

confidence: 99%

Classical and quantum machine learning applications in spintronics

Ghosh¹,

Ghosh²

2022

Preprint

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In this article we demonstrate the applications of classical and quantum machine learning in quantum transport and spintronics. With the help of a two terminal device with magnetic impurity we show how machine learning algorithms can predict the highly non-linear nature of conductance as well as the non-equilibrium spin response function for any random magnetic configuration. We finally describe the applicability of quantum machine learning which has the capability to handle a significantly large configuration space. Our approach is also applicable for molecular systems. These outcomes are crucial in predicting the behaviour of large scale systems where a quantum mechanical calculation is computationally challenging and therefore would play a crucial role in designing nano devices.

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“…Artificial neural networks (ANNs) have widely served as compact models for semiconductor device applications [25][26][27][28][29][30]. Recently, with the surge of machine learning applications, efficient modeling methodologies for ANN training were developed and applied to modeling and simulation problems for advanced transistors [31][32][33][34]. Compared with physical models, the ANN models are fast, adaptable, accurate, and technology-independent for predicting a high nonlinearity of HF noise characteristics in quasi-ballistic MOSFETs.…”

Section: Introductionmentioning

confidence: 99%

“…Figure8shows the noise parameter prediction with the ANN-based equivalent circuit model in 28nm scale quasi-ballistic MOSFETs. The solid line represents the prediction by including the gate and drain shot noise sources of Equation(31), and the dashed line represents the prediction by ignoring the shot noise sources. This figure clearly shows the significant impact of the gate and drain shot noise on the noise parameters, and it demonstrates the good capability of the ANN-based equivalent circuit model in predicting the noise performance.…”

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confidence: 99%

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

Lee

2021

Electronics

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A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-drain tunneling current through the potential barriers. By training a spatial distribution of the thermal channel noise and a Fano factor of the shot noise, underlying physical theories are naturally embedded into the PINN model as prior information. The PINN model shows good capability of predicting the noise performance at high frequencies.

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Speed Up Quantum Transport Device Simulation on Ferroelectric Tunnel Junction With Machine Learning Methods

Cited by 19 publications

References 21 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

Classical and quantum machine learning applications in spintronics

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

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