Parameter Extraction of Solar Module Using the Sooty Tern Optimization Algorithm

Singh, Abha; Sharma, Abhishek; Rajput, Shailendra; Mondal, Amit Kumar; Bose, Amarnath; Ram, Mangey

doi:10.3390/electronics11040564

Cited by 26 publications

(15 citation statements)

References 44 publications

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“…Although the number of process parameters and equations of the EKV model is less than compact BSIM model, but nearly 70 parameters are needed for the description of MOS device and its effects [18]. One of the methods for extracting the parameters of EKV model is converting the BSIM model parameters to EKV, which has been done using the Levenberg-Mrquardt algorithm for 0.18um CMOS process [19]. In this paper, only 26 parameters have been used to develop the process parameters, whose along with 5 optional parameters are listed in Table .1.…”

Section: Process Parametersmentioning

confidence: 99%

EKV Model Base Analog/RF CMOS DesignPre-SPICE Tool

Khademevatan,

Jalali

2023

Preprint

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A novel simplified EKV model base analog/RF CMOS design pre-SPICE tool is presented in this paper. Addition to facilitating the sizing process, this CAD tool can also optimize circuit characteristics. By having a web address, users can access it without installing any software. Usinga graphical and a numerical view, the designer can select degrees of freedom and observe the MOS circuit performance. Through the use of charts versus IC, the graphical view can show tradeoffs in circuit performance in real time. Charts can be displayed simultaneously in both linear and logarithmic scales. ICcrit, is also available and can be displayed on the charts. This tool is not limited to one process and it is possible to select different processes. A methodology for selecting optimum ICsis presented by this tool. It is illustrated through the study of linearity indices, AIP3 and IIP3, in one-stage and two-stage differential amplifiers.

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Section: Process Parametersmentioning

confidence: 99%

EKV Model Base Analog/RF CMOS DesignPre-SPICE Tool

Khademevatan,

Jalali

2023

Preprint

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“…Referring to Equation (10), a chaotic population with a swarm size of N can be produced and represented as a population set of P CS = X CS 1 , . .…”

Section: Modified Initialization Scheme Of Mspsotlpmentioning

confidence: 99%

“…In recent years, metaheuristic search algorithms (MSAs) have emerged as promising solutions to tackle complex optimization problems, such as those reported in [8][9][10][11][12][13]. The excellent global search ability and stochastic characteristic of these MSAs can also be harnessed for training ANN models to solve classification or regression problems.…”

Section: Introductionmentioning

confidence: 99%

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

et al. 2022

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Artificial neural networks (ANNs) have achieved great success in performing machine learning tasks, including classification, regression, prediction, image processing, image recognition, etc., due to their outstanding training, learning, and organizing of data. Conventionally, a gradient-based algorithm known as backpropagation (BP) is frequently used to train the parameters’ value of ANN. However, this method has inherent drawbacks of slow convergence speed, sensitivity to initial solutions, and high tendency to be trapped into local optima. This paper proposes a modified particle swarm optimization (PSO) variant with two-level learning phases to train ANN for image classification. A multi-swarm approach and a social learning scheme are designed into the primary learning phase to enhance the population diversity and the solution quality, respectively. Two modified search operators with different search characteristics are incorporated into the secondary learning phase to improve the algorithm’s robustness in handling various optimization problems. Finally, the proposed algorithm is formulated as a training algorithm of ANN to optimize its neuron weights, biases, and selection of activation function based on the given classification dataset. The ANN model trained by the proposed algorithm is reported to outperform those trained by existing PSO variants in terms of classification accuracy when solving the majority of selected datasets, suggesting its potential applications in challenging real-world problems, such as intelligent condition monitoring of complex industrial systems.

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“…Conventionally, CNN hyperparameters are manually tuned in trial-and-error basis but it is time-consuming. Given their strong global search ability, metaheuristic search algorithms (MSAs) inspired by various natural phenomena [15] (e.g., evolution theory, animal behaviors, physics principles and human activities) are used to solve many complex optimization problems [16], [17], [18], [19], [20], [21], including the hyperparameter tuning of CNN. Arithmetic optimization algorithm (AOA) [14] is an emerging MSA inspired by the distribution behaviors of four major arithmetic operators (i.e., addition, subtraction, multiplication and division).…”

Section: Introductionmentioning

confidence: 99%

Classification of Wafer Defects with Optimized Deep Learning Model

Ang

Juhari

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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Wafer defect inspection is one of the crucial semiconductor processing technologies because it can help to identify the surface defects in the process and eventually improve the yield. Manual inspection using human eye is subjective and long-term fatigue can lead to erroneous classification. Deep learning technology such as convolutional neural network (CNN) is a promising way to achieve automated wafer defect classification. The training of CNN is time consuming and it is nontrivial to fine tune its hyperparameters to achieve good classification performance. In this study, Arithmetic Optimization Algorithm (AOA) is proposed to optimize the CNN hyperparameters, such as momentum, initial learn rate, maximum epochs, L2 regularization, to reduce the burden brought by trial-and-error methods. The hyperparameters of a well-known pretrained model, i.e., GoogleNet, are optimized using AOA to perform wafer defects classification task. Simulation studies report that the AOA-optimized GoogleNet achieves promising accuracy of 91.32% in classifying wafer defects.

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Parameter Extraction of Solar Module Using the Sooty Tern Optimization Algorithm

Cited by 26 publications

References 44 publications

EKV Model Base Analog/RF CMOS DesignPre-SPICE Tool

EKV Model Base Analog/RF CMOS DesignPre-SPICE Tool

A Modified Particle Swarm Optimization Algorithm for Optimizing Artificial Neural Network in Classification Tasks

Classification of Wafer Defects with Optimized Deep Learning Model

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