Browsing by Author "Huang, KY"

Now showing 1 - 14 of 14

A computational efficient method for HBT intermodulation distortions and two-tone characteristics simulation
(2001)
Huang, KY
;
Li, YM
;
Lee, CP
;
Sze, SM
;
電子工程學系及電子研究所
In this paper, a new simulation method for two-tone characteristics calculations and the third-order intercept point (OIP3) of heterojunction bipolar transistor (HBT) in large-scale time domain is proposed. Base on waveform relaxation (WR) and monotone iterative (MI) methods, we solve a set of nonlinear ordinary differential equation (ODE) of equivalent circuit. With this approach, the two-tone characteristics in frequency domain for HBTs were directly computed from time domain result with fast Fourier transform (FFT). Simulation results on a realistic HBT are presented to show the accuracy and efficiency of the method.
A genetic algorithm approach to InGaP/GaAs HBT parameter extraction and RF characterization
(2003-04-01)
Li, YM
;
Cho, YY
;
Wang, CS
;
Huang, KY
;
電子工程學系及電子研究所
In this paper, a computational intelligence technique is applied to extract and simulate the stationary and high-frequency properties of heterojunction bipolar transistors (HBTs). A set of HBT circuit equations formulated with the Gummel-Poon model in time domain is solved with (1) the waveform relaxation (WR), (2) monotone iterative (MI) method, and (3) genetic algorithm (GA) with floating-point operators. The coupled nonlinear equations are decoupled and solved with the WR and MI methods in time domain, and the results obtained are used for the optimization of the characteristics with the GA method. The iteration can be terminated when the final convergent global solution is obtained. The time domain result is used in analyzing the property of the output third-order intercept point (OIP3) with the fast Fourier transform (FFT). Compared with the SPICE result, our simulation results demonstrate that this method is accurate and stable in high frequency simulation. This approach has practical applications in HBT characterization and radio frequency (RF) circuit optimal design.
A novel numerical approach to heterojunction bipolar transistors circuit simulation
(2003-05-15)
Li, YM
;
Huang, KY
;
電子工程學系及電子研究所
;
友訊交大聯合研發中心
;
Department of Electronics Engineering and Institute of Electronics
In this paper we present a novel computational method for calculating the heterojunction bipolar transistor (HBT) physical characteristics in the time domain. To calculate the HBT high frequency properties, the Gummel-Poon equivalent circuit model is applied to replace the HBT in the circuit and a set of governing ordinary differential equations (ODEs) is formulated. We directly decouple the system ODEs and solve each decoupled ODE with the monotone iterative method in the time domain. This solution methodology proposed here has been applied to semiconductor device simulation by us earlier, and we find this method for the HBT simulation has good accuracy and converges globally. Compared with the HSPICE circuit simulator results, our results present the accuracy, efficiency, and robustness of the method. (C) 2002 Elsevier Science B.V. All rights reserved.
A time-domain approach to simulation and characterization of RF HBT two-tone intermodulation distortion
(2003-10-01)
Huang, KY
;
Li, YM
;
Lee, CP
;
電子工程學系及電子研究所
;
友訊交大聯合研發中心
In this paper, we evaluate the two-tone intermodulation distortion for heterojunction bipolar transistors (HBTs) operated at RF. We directly solve the nonlinear differential equations of the HBT large-signal model in time domain by employing the waveform-relaxation and monotone-iterative methods. Based on time-domain results, sinusoidal waveform outputs are transformed into the frequency domain with the fast Fourier transform. Furthermore, the output third-order intercept-point values of the HBT are computed with the spectra. Results for a fabricated InGaP HBT under different testing conditions are reported and compared among the HSPICE results, the results with harmonic balance methodology, and the measured data. Comparisons among these results show that our method demonstrates its superiority over the conventional approaches. This characterization alternative has allowed us to study RF device properties, perform thermal consumption and sensitivity analysis, and extract model parameters.
Computer simulation of multifinger heterojunction bipolar transistor with self-heating and thermal coupling models
(2004-08-01)
Huang, KY
;
Li, YM
;
Lee, CP
;
電子工程學系及電子研究所
;
友訊交大聯合研發中心
In this paper, we simulate the nonlinearity of a multifinger heterojunction bipolar transistor (HBT) operated at radio frequency (RF). We directly solve the nonlinear differential equations of the HBT large-signal model with the electrical-thermal feedback equations in time domain using the waveform relaxation (WR) and monotone iterative (MI) methods. The temperature dependence of energy band gap (E-g), current gain, saturation current and thermal conductivity are also taken into consideration. With the developed simulator, the power-added efficiency (PAE), 1-dB compression point (P1-dB) and output third-order intercept point (OIP3) of a three-finger HBT are calculated. Our results illustrate the effects of self-heating and thermal coupling among different fingers play important roles in the nonlinearity of the multifinger power transistors. Furthermore, the proposed method allows us to evaluate the thermal effects on linearity of the multifinger power transistors and perform optimum design for these devices. (C) 2004 Elsevier B.V. All rights reserved.
Neural computing for seismic principal components analysis
(1997)
Huang, KY
;
交大名義發表
;
資訊工程學系
;
National Chiao Tung University
;
Department of Computer Science
The neural network of the unsupervised generalized Hebbian algorithm (GHA) is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. The theorem about the effect of adding one extra point along the direction of the eigenvector is proposed to help the interpretations that more uniform data vectors along one principal eigenvector direction can enhance the eigenvalue. Diffraction pattern, fault pattern, bright spot pattern and real seismogram are in the experiments. From analyses the principal components can show the high amplitude, polarity reversal, and low frequency wavelet in the detection of seismic anamalies and can improve seismic interpretations.
Neural implementation of fuzzy K-NN classification for seismic pattern recognition
(1996)
Huang, KY
;
Yuan, YW
;
資訊工程學系
;
Department of Computer Science
Neural network and tree automaton for seismic pattern recognition
(2004)
Huang, KY
;
Chao, YH
;
資訊工程學系
;
Department of Computer Science
We combine neural network and syntactic pattern recognition, and propose a tree automaton system for the recognition of structural seismic patterns in a seismogram Multilayer perceptron of the neural network is used for the identification of subpatterns, then a tree representation of the structural seismic pattern is constructed. We use three kinds of modified bottom-up structure preserved error correcting tree automata to recognize the tree representation of syntactic pattern, and propose a new top-down error correcting tree automaton to recognize non-structural preserved seismic pattern. In the experiments, the system is applied to the simulated and the real seismic bright spot patterns. The recognition result can improve seismic interpretation.
Neural network for robust recognition of seismic patterns
(2001)
Huang, KY
;
資訊工程學系
;
Department of Computer Science
The multilayer perceptron neural network is trained as a classifier and is applied to the recognition of seismic patterns. The principle of training the multilayer perceptron is described. Three classes of seismic patterns are analyzed in the experiment: bright spot, pinch-out, and horizontal reflection patterns. Seven moments that are invariant to translation, rotation, and scale, are employed for feature generation of each seismic pattern. The training set includes noise-free, low-noise, and misclassified seismic patterns. The test set includes seismic patterns with various noise levels. The multilayer perceptron is initially, trained with the training set of noise-free and low-noise seismic patterns. After convergence of the training, the network is applied to the classification of the test set of noisy seismic patterns. Some misclassified patterns with higher noise level are added to the training set for retraining, The classification and training process is repeated through several stages. This retraining can significantly, improve the robustness of the network. The converged network at each training stage is applied to the real seismic data at Mississippi Canyon, the bright spot pattern can be detected after the retraining at the higher noise level. From these experiments, the multilayer perceptron is shown to have the capability of robust recognition of seismic patterns.
Neural network for seismic horizon picking
(1998)
Huang, KY
;
資訊工程學系
;
Department of Computer Science
Hopfield neural network can solve the optimization problem. We use Hopfield net to the seismic horizon picking. the peak position of each seismic wavelet is corresponding to one neuron. We transform the constraints of the detecting local horizon patterns and the constraints of extracting one horizon each time into the system energy function. From the theory of Hopfield net, changing the values of neurons can decrease the energy. The system will be stable until the values of neurons are not changed One horizon is extracted by using the algorithm at each time. Remove the extracted horizon from the original seismic data and extract the next horizon until the last horizon, is extracted From the experimental results in bright spot, the picked horizons can match the visual inspection.
Neural networks for seismic principal components analysis
(1999-01-01)
Huang, KY
;
資訊工程學系
;
Department of Computer Science
The neural network, using an unsupervised generalized Hebbian algorithm (GHA), is adopted to find the principal eigenvectors of a covariance matrix in different kinds of seismograms. We have shown that the extensive computer results of the principal components analysis (PCA) using the neural net of GHA can extract the information of seismic reflection layers and uniform neighboring traces. The analyzed seismic data are the seismic traces with 20-, 25-, and 30-Hz Ricker wavelets, the fault, the reflection and diffraction patterns after normal moveout (NMO) correction, the bright spot pattern, and the real seismogram at Mississippi Canyon. The properties of high amplitude, low frequency, and polarity reversal can be shown from the projections on the principal eigenvectors. For PCA, a theorem is proposed, which states that adding an extra point along the direction of the existing eigenvector can enhance that eigenvector. The theorem is applied to the interpretation of a fault seismogram and the uniform property of other seismograms. The PCA also provides a significant seismic data compression.
Numerical simulation of self-heating InGaP/GaAs heterojunction bipolar transistors
(2005)
Li, YM
;
Huang, KY
;
電子工程學系及電子研究所
;
友訊交大聯合研發中心
;
Department of Electronics Engineering and Institute of Electronics
We numerically simulate effects of the self-heating on the current-voltage characteristics of InGaP/GaAs heterojunction bipolar transistors (HBTs). A set of coupled nonlinear ordinary differential equations (ODEs) of the equivalent circuit of HBT is formed and solved numerically in the large-signal time domain. We decouple the corresponding ODEs using the waveform relaxation method and solve them with the monotone iterative method. The temperature-dependent energy band gap, the current gain, the saturation current, and the thermal conductivity are considered in the model formulation. The power-added efficiency and the 1-dB compression point of a three-finger HBT are calculated. This approach successfully explores the self-heating and the thermal coupling phenomena of the three-finger transistors under high power and high frequency conditions. The numerical algorithm reported here can be incorporated into electronic computer-aided design software to simulate ultra-large scale integrated and radio frequency circuits.
Seismic pattern recognition using neural network and tree automaton
(2004)
Huang, KY
;
Chao, YH
;
資訊工程學系
;
Department of Computer Science
We combine neural network and syntactic pattern recognition, and propose a tree automaton system for the recognition of structural seismic patterns in a seismogram. Multilayer perceptron of the neural network is used for the identification of subpatterns, then a tree representation of the structural seismic pattern is constructed. We use three kinds of modified bottom-up structure preserved error correcting tree representation of syntactic automata to recognize the tree pattern, and propose a new top-down error correcting tree automaton to recognize non-structural preserved seismic pattern. In the experiments, the system is applied to the simulated and the real seismic bright spot patterns. The recognition result can improve seismic interpretation.
VLSI cell placement on arbitrarily-shaped rectilinear regions using neural networks with calibration nodes - Comments
(1997-04-01)
Huang, KY
;
交大名義發表
;
資訊工程學系
;
National Chiao Tung University
;
Department of Computer Science