Browsing by Author "Cho, YY"

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A genetic algorithm approach to InGaP/GaAs HBT parameter extraction and RF characterization
(2003-04-01)
Li, YM
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Cho, YY
;
Wang, CS
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Huang, KY
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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.
Intelligent BSIM4 model parameter extraction for sub-100 nm MOSFET era
(2004-04-01)
Li, YM
;
Cho, YY
;
友訊交大聯合研發中心
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D Link NCTU Joint Res Ctr
We present. in this paper, an intelligent extraction technique for obtaining a set of optimal model parameters of the Berkeley short-channel insulated gate field effect transistor model 4 (BSIM4) for sub-100nm metal-oxide-semiconductor field effect transistors (MOSFETs). Based on the genetic algorithm (GA), the monotone iterative Levenberg-Marquardt (MI-LM) method, and the neural network (NN) algorithm, this novel approach can perform simultaneous BSIM4 parameter extraction with more than 16 sub-100 nm MOSFETs in a global sense. Before extraction, all input measured I-V data are preprocessed by statistical reduction and sampling procedures. The GA and MI-LM method are then applied to calculate all parameters. The NN algorithm is used to trace the errors of I-V curves and their first derivatives, and also to inspect the variations of physical quantities. Once parameters are found, the postprocess will identify a sensible searching path so that the solution engine continues evolution until parameters reach specified stopping criteria. Good accuracy is obtained for the 90 nm NMOSFETs by several testing cases. Compared with manual fitting processes, this methodology overcomes the conventional inconvenience and is cost-effective in automatic parameter extraction. It bridges device fabrication technology and system-on-a-chip (SOC) design.