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授課教師:陳文山 學生:蘇修賢 Neural Network Theory and eneralities Neural Network Applications in Microwave Modeling and Design Conclusion Neural Network Theory and Generalities Basics of Neural Networks General Neural Network Modeling Approach Neural Networks for Inverse Modeling Problem Basics of Neural Networks 1. A typical neural network structure comprises two types of basic components,the processing elements and the interconnections between them. 2. The processing elements are called neurons and the connections between the neurons are known as links or synapses. 3. Every link has a corresponding weight parameter associated with it. Each neuron receives stimuli from other neurons connected to it, processes the information, and produces an output. 4. Multilayer perceptron (MLP) is a popularly used neural network structure. In the MLP neural network, y1 1 y2 2 ym … Layer 3 (Output Layer) m 1 2 3 … q Layer 2 (Hidden Layer) 1 2 3 … n Layer 1 (Input Layer) x1 x2 x3 xn Figure 1. A three-layer perceptron neural network structure with an input layer, a hidden layer, and an output layer. Neural Network Theory and Generalities Basics of Neural Networks General Neural Network Modeling Approach Neural Networks for Inverse Modeling Problem General Neural Network Modeling Approach 1. To develop a neural network model, we first define the input and output variables of a device or a structure. We then generate IO data using EM simulation, physics-based simulation, or measurement. 2. The generated data, training data, are used to train the neural network. Once the model is trained, it can be incorporated into a circuit simulator for fast and accurate simulation and optimization. 3. This allows circuit-level simulation speed with EM-level accuracy. This process is illustrated in Figure 2. For a given component, Figure 2. Illustration of fast circuit optimization where a spiral inductor component is represented by a neural network model, avoiding repeated EM simulation when geometrical parameters are changed. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Neural Network Theory and Generalities Basics of Neural Networks General Neural Network Modeling Approach Neural Networks for Inverse Modeling Problem Neural Networks for Inverse Modeling Problem 1. To train the neural network inverse model, we swap the generated data so that electrical parameters become training data for neural network inputs and geometric parameters become training data for neural network outputs. 2. Using these data, the trained neural network becomes a direct inverse model. The model is then used to obtain values of geometric design variables from an electrical parameter in a single model evaluation. 3. Unlike the forward model in which the input to output mapping (from geometric parameter to electrical parameter) is usually a one-to-one mapping, the inverse model often encounters the problem of nonunique solutions. 4. This problem also causes difficulties during training, because the same input values to the inverse model will lead to different values at the output (multivalued solutions). Neural Networks for Inverse Modeling Problem Development of Neural Network Inverse Models for Waveguide Filter Waveguide Filter Design Using Inverse Models Neural Network for Parametric Modeling of a Complete Microwave Filter Neural Network for Correction of Nonlinear Device Models Neural Network Theory and eneralities Neural Network Applications in Microwave Modeling and Design Conclusion Development of Neural Network Inverse Models for Waveguide Filter 1.We develop neural network inverse models for waveguide filter. These inverse models will be used to design filters with the inverse approach. 2. The filter is decomposed into three different modules, each representing a separate filter junction. The three models are the IO iris, the internal coupling iris, and the tuning screws. 3.Figure 3 shows a diagram of a waveguide filter revealing various dimensions of the models. Symbol M12 represents the coupling term between cavity 1 and cavity 2. ther coupling terms are also defined similarly. Figure 3. Diagram of a circular waveguide filter showing arious geometrical variables. M12 represents the coupling term between cavity 1 and cavity 2. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Lr Pv CD Ph w0 (a) Pin R Lv CD Lh w0 (b) Pv M23 Ph M14 Ph CD Lh w0 Lc M12 (c) Figure 4. Neural network inverse models representing junctions of a waveguide filter. (a) Input-output iris model, (b) internal coupling iris model, and (c) tuning screw model. Symbols CD and vo represent cavity diameter and center frequency, respectively. P Neural Networks for Inverse Modeling Problem Development of Neural Network Inverse Models for Waveguide Filter Waveguide Filter Design Using Inverse Models Neural Network for Parametric Modeling of a Complete Microwave Filter Neural Network for Correction of Nonlinear Device Models Waveguide Filter Design Using Inverse Models 1. The filter’s center frequency is 12.155 GHz, bandwidth is 64 MHz and cavity diameter is chosen to be 1.072 in. The normalized ideal coupling values are obtained from coupling matrix synthesis, as shown in Figure 5. 2. Figure 6 presents the response of the tuned filter and compares with the ideal one showing a perfect match between each other. Geometrical Dimensions of Filter Neural Inverse Model for Filter Ideal Coupling Values Coupling Matrix Synthesis Filter Specification Figure 4. Design approach using advanced neural network inverse models. Figure 5. Comparison of the 6pole filter response with ideal filter response. The filter was designed, fabricated, tuned and then measured to obtain the dimensions. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Neural Networks for Inverse Modeling Problem Development of Neural Network Inverse Models for Waveguide Filter Waveguide Filter Design Using Inverse Models Neural Network for Parametric Modeling of a Complete Microwave Filter Neural Network for Correction of Nonlinear Device Models Neural Network for Parametric Modeling of a Complete Microwave Filter 1.A parametric model for a complete filter requires that the model has many geometric variables. In the conventional approach, change of a geometric variable requires EM resimulation of the whole filter. 2.Here, we develop a fast neural network model for this purpose. The geometric variables will be formulated as neural network input neurons. Figure 6. Diagram of a side-coupled filter. (a) Side view. (b) Top view. From This type of filter offers significant performance improvement and finds its application in the satellite multiplexers with extremely stringent mass, size, and thermal requirements. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Figure 7. Comparisons of reflection coefficients of a sidecoupled circular waveguide dual-mode filter obtained using the neural network model and the EM model. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Neural Networks for Inverse Modeling Problem Development of Neural Network Inverse Models for Waveguide Filter Waveguide Filter Design Using Inverse Models Neural Network for Parametric Modeling of a Complete Microwave Filter Neural Network for Correction of Nonlinear Device Models Neural Network for Correction of Nonlinear Device Models 1.Another useful application of neural network is to map or repair an existing model to match a new device. This process is called Neuro–Space Mapping (Neuro-SM). 2.The starting point for the Neuro-SM is when the existing/available device model (coarse model) cannot match the data of a new device (fine model). Figure 8. (a) The physical structure of a HEMT device used for the physics-based device simulator (the fine model). (b) The Neuro-SM HEMT intrinsic nonlinear model. The coarse model is an existing/available equivalent circuit model. The neural network mapping is incorporated as the controlling functions of the controlled sources. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Figure 9. Comparison between the HEMT device data from the fine model (MINIMOS), the existing model (without mapping), and the Neuro-SM model in the HEMT example . (a) dc and (b) S-parameters at four different bias combinations of gate and drain voltages. 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Figure 10. Doherty amplifier module used to generate training data for neural networks for power amplifier behavioral modeling: the transistor package contains two separate transistors, which are configured as a Doherty amplifier . 資料來源: H. Kabir, Z. Lei, Y. Ming, P. Aaen, J. Wood, and Q. J. Zhang, “Smart Modeling of Microwave” Devices IEEE Microwave Magazine , Vol. 11 (2009 ) 105-108 Neural Network Theory and eneralities Neural Network Applications in Microwave Modeling and Design Conclusion Conclusion Neural networks are fast to evaluate, and the neural network formulas are easy to implement into microwave CAD. The simplicity of adding input neurons or hidden neurons makes neural network flexible in handling functions of different dimensions and of different degree of nonlinearity. Neural networks are helpful in developing parametric or scalable models for passive and active microwave devices. 心得 關於此濾波器讓我學系到了類神經網路的相關知識。 此濾波器在類神經網路之應用,有著不錯的增益。