Modeling of silicon oxynitride etch microtrenching using genetic algorithm and neural network

Kim, Byungwhan; Bae, Junki; Lee, Byung Teak

doi:10.1016/j.mee.2005.12.001

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…A statistical experimental design was used to make the NN training most cost effective, whereas GA was used to find out the NN weight factors. [134][135][136] Kim et al 137,138 adopted a GA based methodology that enables a user to design a modal transducer for two-dimensional structures with arbitrary geometry and boundary conditions. The gain distribution was approximated by optimising electrode patterns, lamination angles and relative poling directions of the multilayered polyvinylidene fluoride transducer.…”

Section: Other Polymer Applicationsmentioning

confidence: 99%

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Mitra¹

2008

International Materials Reviews

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Genetic algorithms (GAs) belong to the broader family of generic population based metaheuristic optimisation algorithms called evolutionary algorithms. These are one of the biology inspired computing techniques that emulate the basic Darwinian concept of natural selection. Because of their many positive attributes such as ease of use, versatility and robustness, GAs have become important in materials research very rapidly since their inception. Though going through a major stage of theoretical development, GAs have been successfully applied in many fields such as polymer production, polymer design, polymer processing, polymer laminates and other polymers, as well as non-polymer applications in recent times. The present review attempts to present the state of the art in this area. The review is broadly divided into three sections: fundamentals of GAs, the use of GAs in polymeric materials and the use of GAs in non-polymer applications. First, the fundamentals of GAs are presented to provide some glimpse of the technique; this is followed by presentations on applications of GAs in various polymeric fields, such as in polymer production, polymer scheduling, polymer design, polymer laminates and other polymer related applications and non-polymeric fields, e.g. metallic laminates and other metallic material applications. A thorough literature review and critical analysis of the review is provided and attempts have been made to portray how evolutionary optimisation techniques, GAs in particular, are used across a variety of applications.

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Section: Other Polymer Applicationsmentioning

confidence: 99%

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Mitra¹

2008

International Materials Reviews

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“…The first layer is fully connected to the pattern layer, whose output is a measure of the distance of the input from the stored patterns. Each pattern layer unit is connected to the two neurons in the summation layer, known as S summation neuron and D summation neuron [12] . The S summation neuron computes the sum of the weighted outputs of the pattern layer while the D summation neuron calculates the unweighted outputs of the pattern neurons.…”

Section: Neural Network Architecture and Training Algorithmmentioning

confidence: 99%

Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method

Haidar¹,

Mohamed²,

Hussain³

2008

American J. of Applied Sciences

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Vulnerability assessment in power systems is important so as to determine how vulnerable a power system in case of any unforeseen catastrophic events. This paper presents the application of Radial Basis Function Neural Network (RBFNN) for vulnerability assessment of power system incorporating a new proposed feature extraction method named as the Neural Network Weight Extraction (NNWE) for dimensionality reduction of input data. The performance of the RBFNN is compared with the Multi Layer Perceptron Neural Network (MLPNN) so as to evaluate the effectiveness of the RBFNN in assessing the vulnerability of a power system based on the indices, power system loss and possible loss of load. In this study, vulnerability analysis simulations were carried out on the IEEE 300 bus test system using the Power System Analysis Toolbox and the development of neural network models were implemented in MATLAB version 7. Test results prove that the RBFNN give better vulnerability assessment performance than the multilayer perceptron neural network in terms of accuracy and training time. The proposed feature extraction method decreases the training time drastically from hours to less than seconds, this bound to influence the vulnerability classification and increase the speed of convergence. It is also concluded that the reduction in error is achieved by using PSL as an output variable of ANN, in all the cases the error of RBFNN output by PSL is less than 4.87% which is well within tolerable limits.

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“…Moreover, semiconductor and integrated circuit manufacturing industry is highly suitable to be integrated with DL techniques because the semiconductor manufacturing process encounters a large number of parameters and a various number of procedures that are inevitable to be performed manually by engineers. There is some prior research based on the integration of ML with semiconductor manufacturing [27][28][29][30]. In [31], a ML algorithm was reported for defect detection.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

Akbar

Sung

2021

IEEE Access

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Device simulation has been explored and industrialized for over 40 years; however, it still requires huge computational cost. Therefore, it can be further advanced using deep learning (DL) algorithms. We for the first time report an efficient and accurate DL approach with device simulation for gate-all-around silicon nanowire metal-oxide-semiconductor field-effect transistors (MOSFETs) to predict electrical characteristics of device induced by work function fluctuation. By using three different DL models: artificial neural network (ANN), convolutional neural network (CNN), and long short term memory (LSTM), the variability of threshold voltage, on-current and off-current is predicted with respect to different metal-grain number and location of the low and high values of work function. The comparison is established among the ANN, CNN and the LSTM models and results depict that the CNN model outperforms in terms of the root mean squared error and the percentage error rate. The integration of device simulation with DL models exhibits the characteristic estimation of the explored device efficiently; and, the accurate prediction from the DL models can accelerate the process of device simulation. Notably, the DL approach is able to extract crucial electrical characteristics of a complicated device accurately with 2% error in a cost-effective manner computationally.

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Modeling of silicon oxynitride etch microtrenching using genetic algorithm and neural network

Cited by 16 publications

References 22 publications

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Genetic algorithms in polymeric material production, design, processing and other applications: a review

Vulnerability Assessment of Power System Using Radial Basis Function Neural Network and a New Feature Extraction Method

Deep Learning Algorithms for the Work Function Fluctuation of Random Nanosized Metal Grains on Gate-All-Around Silicon Nanowire MOSFETs

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