论文信息 - A Neural Network Approach to the Modeling of Heterojunction Bipolar Transistors from S-Parameter Data

A Neural Network Approach to the Modeling of Heterojunction Bipolar Transistors from S-Parameter Data

Artificial neural networks have gained attention as a fast, efficient, flexible and accurate tool in the areas of microwave modeling, simulation and optimization. In this paper, a novel neural network approach is proposed for the modeling of Heterojunction Bipolar Transistors (HBT) directly from their S-Parameter data. The neural network structure incorporates bias current and bias voltage as inputs. This enables us to use the same neural model under different bias conditions. The proposed technique provides reliable neural transistor models, while significantly reducing the cost effort and complexity involved in the modeling of HBT.

[1] Y. Awano,et al. Monte Carlo simulation of AlGaAs/GaAs heterojunction bipolar transistors , 1984, IEEE Electron Device Letters.

[2] S. Prasad,et al. Creating neural network based microwave circuit models for analysis and synthesis , 1997, Proceedings of 1997 Asia-Pacific Microwave Conference.

[3] Dimitris Pavlidis,et al. Evaluation of the factors determining HBT high-frequency performance by direct analysis of S-parameter data , 1992 .

[4] Ce-Jun Wei,et al. Direct extraction of equivalent circuit parameters for heterojunction bipolar transistors , 1995 .

[5] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .

[6] Fang Wang,et al. Knowledge based neural models for microwave design , 1997, IMS 1997.

[7] Alberto L. Sangiovanni-Vincentelli,et al. Efficient Parallel Learning Algorithms for Neural Networks , 1988, NIPS.

[8] Roberto Battiti,et al. First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[9] G. L. Creech,et al. Artificial neural networks for accurate microwave CAD applications , 1996, 1996 IEEE MTT-S International Microwave Symposium Digest.