Prediction of performance parameters of stratified TES tank using artificial neural network

2018 ◽  
Author(s):  
Afzal Ahmed Soomro ◽  
Ainul Akmar Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Performance Parameters ◽  
Artificial Neural

Artificial Neural Network and Genetic Algorithm Based Hybrid Intelligence for Performance Optimization of Novel Inverted Funnel Shaped Fin Shaped Field Effect Transistor with Gate Stack High-k Dielectric

2020 ◽  
Vol 15 (11) ◽  
pp. 1385-1394
Author(s):  
Gurpurneet Kaur ◽  
Sandeep Singh Gill ◽  
Munish Rattan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Performance Optimization ◽  
Field Effect ◽  
Field Effect Transistors ◽  
Semiconductor Industry ◽  
Performance Parameters ◽  
Short Channel ◽  
Artificial Neural

Today, Fin shaped Field Effect Transistors (FinFETs) are the foundation of the sub-nanometer technology node. The semiconductor industry endorses it in low-power (LP) and high-performance (HP) applications due to its better electrostatic control and exceptional scalability. In this paper, the structure of an inverted funnel-shaped FinFET device with a high-k stacked gate has been optimized using integrated Artificial Neural Network (ANN) and genetic algorithm (GA) approach. The comparative analysis of rectangular FinFET, trapezoidal FinFET and proposed novel shaped FinFET has also been explored. The electrical and analog performance parameters of the novel device present better performance results with respect to the other two transistors. In ANN training, the three datasets have been created by varying the metrics such as equivalent oxide thickness (EOT) and dielectric constant (k) of novel shaped FinFET device in Technology computeraided design simulator (TCAD). The amalgamation technique of ANN and GA optimization provides diminished Subthreshold Swing (SS), reduced off-current (IOFF), enhanced on-current (ION) and improved current ratio (ION/IOFF) corresponding to the optimal value of EOT and k. The new structure designed and simulated with the optimal amount of EOT and k results in outstanding performance parameters. The device metrics values, SS of 62.1 mV/dec, IOFF of 6.56×10-11, ION of 3.938×10-5 and ION/IOFF of 5.95×105 indicate that optimized device has suppressed Short Channel Effects (SCEs). The average deviatION of 3.48% between the value of ANN-GA optimized results obtained through MATLAB and TCAD simulated performance parameters justify the effectiveness of proposed FinFET.

scholarly journals A Generalized ANN Model for Analyzing and Synthesizing Rectangular, Circular, and Triangular Microstrip Antennas

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Microstrip Antennas ◽  
Computational Technique ◽  
Performance Parameters ◽  
Ann Model ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Good Agreement

Since last one decade, artificial neural network (ANN) models have been used as fast computational technique for different performance parameters of microstrip antennas. Recently, the concept of creating a generalized neural approach for different performance parameters has been motivated in microstrip antennas. This paper illustrates a generalized neural approach for analyzing and synthesizing the rectangular, circular, and triangular MSAs, simultaneously. Such approach is very much required for the antenna designers for getting instant answer for the required parameters. Here, total seven performance parameters of three different MSAs are computed using generalized neural approach as such a method is rarely available in the open literature even for computing more than three performance parameters, simultaneously. The results thus obtained are in very good agreement with the measured results available in the referenced literature for all seven cases.

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