Implementation of a Neural Network HEMT Model Into ADS
Neural network algorithms have been applied to various areas of engineering and computer science. A high electron-mobility transistor (HEMT) large-signal model has been implemented in the Advanced Design System (ADS) software package using a multilayered neural network. The neural network model was...
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| Published in | Microwave Journal Vol. 44; no. 11; pp. 66 - 76+78 |
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| Main Authors | , , |
| Format | Journal Article Trade Publication Article |
| Language | English |
| Published |
Dedham
Horizon House Publications, Inc
01.11.2001
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| Edition | International ed. |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0192-6225 |
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| Abstract | Neural network algorithms have been applied to various areas of engineering and computer science. A high electron-mobility transistor (HEMT) large-signal model has been implemented in the Advanced Design System (ADS) software package using a multilayered neural network. The neural network model was trained to the drain current, gate-source capacitance and gate-drain capacitance characteristics of an Angelov model previously developed. Training was done using the back-propagation algorithm, which achieved excellent results for the model. Linear and harmonic balance simulations were performed to compare the linear and power performance of the neural network model to the Angelov model, without any additional optimization after the initial training. |
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| AbstractList | This article presents an approach for implementing a NN algorithm to develop a large-signal HEMT model to be used in ADS by circuit designers. A typical large-signal model is shown in Figure 2. The NN model proposed in this article implements NN algorithms for Isub dsc, Csub gc and Csub gd with Isub ds being the most nonlinear element in the circuit, and Csub gc and Csub gd being secondary nonlinear elements that affect the accuracy of the model as suggested by [H. This article presents an approach for implementing a NN algorithm to develop a large-signal HEMT model to be used in ADS by circuit designers. A typical large-signal model is shown in Figure 2. The NN model proposed in this article implements NN algorithms for I^sub dsc^, C^sub gc^ and C^sub gd^ with I^sub ds^ being the most nonlinear element in the circuit, and C^sub gc^ and C^sub gd^ being secondary nonlinear elements that affect the accuracy of the model as suggested by [H. Neural network algorithms have been applied to various areas of engineering and computer science. A high electron-mobility transistor (HEMT) large-signal model has been implemented in the Advanced Design System (ADS) software package using a multilayered neural network. The neural network model was trained to the drain current, gate-source capacitance and gate-drain capacitance characteristics of an Angelov model previously developed. Training was done using the back-propagation algorithm, which achieved excellent results for the model. Linear and harmonic balance simulations were performed to compare the linear and power performance of the neural network model to the Angelov model, without any additional optimization after the initial training. |
| Audience | Trade |
| Author | Miller, Eric V White, Carl Thompson, Willie L. II |
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| Snippet | Neural network algorithms have been applied to various areas of engineering and computer science. A high electron-mobility transistor (HEMT) large-signal model... This article presents an approach for implementing a NN algorithm to develop a large-signal HEMT model to be used in ADS by circuit designers. A typical... |
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| SubjectTerms | Accuracy Algorithms Back propagation Colleges & universities Computer engineering Computer software industry Design Design and construction High performance systems Instrument industry Mathematical functions Microwave communications Neural networks Optimization Physics Product information Propagation Simulation Software Systems design Transistors |
| Title | Implementation of a Neural Network HEMT Model Into ADS |
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