基于神经网络的微波射频MOSFET器件建模
|
摘要
随着无线通信技术的不断发展,射频集成电路在其中扮演越来越重要的角色。CMOS工艺已经成为制作可靠的射频电路最合适的工艺之一,CMOS工艺的不断进步已经使MOSFET器件具有良好的微波射频性能。相对比其它工艺,CMOS工艺还具有制造工艺成熟、成本低、功耗低、集成度高、模拟／数字兼容性好和按比例缩放特性一致等优势。为了确保所需频段的电路性能和缩短研制时间,器件模型是一个非常关键的因素。神经网络作为一种非传统建模方法已经广泛用于射频/微波计算机辅助设计。传统建模方法中的数值建模方法通常计算非常耗时；而分析方法在表征新的器件时存在较大的困难；经验模型在适用器件范围和模型精度都是有限的。相对比上述传统建模方法而言,神经网络建模方法是可供选择的有效方法,而且神经网络建模方法比传统建模方法更能适应新器件模型的开发。本论文的主要目的就是研究基于神经网络的MOSFET器件建模,主要内容包括：
MOSFET器件表征及主要建模方法汇总。研究了MOSFET器件结构及其工作原理、MOSFET器件的高频特性及其表征。总结了各种MOSFET器件建模方法,重点分析了各种基于神经网络的器件建模方法。
基于神经网络的MOSFET器件小信号建模技术研究。研究了MOSFET器件的测试结构及去嵌方法、小信号等效电路模型及参数提取方法。利用直接参数提取方法提取小信号模型参数并优化,建立了偏置相关的经验模型。提出一种基于神经网络-空间映射技术的MOSFET偏置相关的小信号建模方法,利用所提出的方法对130纳米工艺MOSFET器件进行小信号建模,在频率100MHz-40GHz范围内,模拟结果和测量结果吻合很好。
基于神经网络的MOSFET器件直流建模技术研究。提出了两种基于神经网络-空间映射技术的MOSFET直流特性建模的方法：第一种方法利用传统的神经网络-空间映射技术对直流特性建模也称之为Neuro-SM模型；第二种方法是将传统神经网络-空间映射技术与先验知识注入及源差分法结合起来也称之为NSM-PKI-D模型。推导了上述两种方法中MOSFET器件跨导和漏导的公式。漏电流特性、跨导和漏导的模拟和测量结果比较证明了我们模型的有效性。
基于神经网络的MOSFET器件大信号建模技术研究。提出了一种将等效电路和神经网络-空间映射建模技术相结合的MOSFET大信号建模方法,该方法不仅保持了模型各部分的物理含义,而且结合了神经网络-空间映射建模技术的优势。为了解决DC/AC色散问题,两个基于神经网络-空间映射的Neuro-SM模型分别用来模拟在直流和射频不同情况下的漏电流。栅长为0.13μm、单栅指的栅宽为5μm、共20个栅指的MOSFET器件的直流及各种不同偏置点下小信号和大信号模拟结果与数据的比较验证了我们模型的有效性。与经验模型的比较进一步验证了我们的模型具有更好的精度。
With the continuous development of wireless communication technology, radio frequency integrated circuit plays an increasingly important role in it. Bulk CMOS technology has become one of the most feasible candidates for building reliable circuits for RF applications. The progress of CMOS technology has made MOSFET transistors show excellent microwave performance. In addition, CMOS technology has such advantages relative to other technologies as mature manufacturing process, low cost, low power, high integration, good integration with high performance digital circuits and high-speed analog circuits, and successful scalability. In order to ensure the circuit performance for the required frequency bands and also to shorten the ratio of time to market, device models are very critical. Artificial neural networks (ANNs) have already been applied to RF and Microwave computer-aided design (CAD) tasks as an unconventional alternative. Neuromodeling is efficient in comparison to conventional modeling methods, such as numerical modeling methods, which could be computationally expensive, or analytical methods, which could be difficult to obtain for new devices, or empirical models, whose range and accuracy could be limited. Furthermore, neural models are easier to develop for new devices than conventional models. Thus, neural network based MOSFET modeling is studied in this thesis. The main content of the thesis is divided into four parts as follows:
Firstly, the characterization and principal modeling method for MOSFET is reviewed. The structure and operation of MOSFET is presented. The characteristic and characterization of MOSFET at high frequency is also studied. Various kinds of modeling methods for MOSFET are summarized, where we focus on neural network based modeling method.
Secondly, neural network based small-signal modeling for MOSFET is studied. The test structure and de-embedding method of MOSFET are presented. Small-signal equivalent circuit and direct parameter extraction method for MOSFET are also studied. Parameters in equivalent circuit are extracted and then optimized, and corresponding bias-dependent empirical model is determined. Bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100MHz-40GHz confirming the validity and effectiveness of our approach.
Thirdly, neural network based DC modeling for MOSFET is studied. Two approaches for modeling DC characteristics for MOSFET based on neuro-space mapping (SM) are proposed. The first approach makes use of classical neuro-SM technology, while the second combines neuro-SM with prior knowledge input and source difference method. The formulas for obtaining the transconductance and output conductance in two approaches are derived. TheⅠ-Ⅴcharacteristics as well as their conductances obtained by the formulas in two approaches are compared to the measured data. Experimental results, which confirm the validity of our approaches, are also presented.
Finally, neural network based large-signal modeling for MOSFET is studied. A large-signal modeling approach based on the combination of equivalent circuit and neuro-space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro-space mapping based models are employed to model the drain current at DC and RF conditions respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13μm gate length,5μm gate width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small-signal and nonlinear operation. Comparison of DC, S-parameter and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model.
MOSFET器件表征及主要建模方法汇总。研究了MOSFET器件结构及其工作原理、MOSFET器件的高频特性及其表征。总结了各种MOSFET器件建模方法,重点分析了各种基于神经网络的器件建模方法。
基于神经网络的MOSFET器件小信号建模技术研究。研究了MOSFET器件的测试结构及去嵌方法、小信号等效电路模型及参数提取方法。利用直接参数提取方法提取小信号模型参数并优化,建立了偏置相关的经验模型。提出一种基于神经网络-空间映射技术的MOSFET偏置相关的小信号建模方法,利用所提出的方法对130纳米工艺MOSFET器件进行小信号建模,在频率100MHz-40GHz范围内,模拟结果和测量结果吻合很好。
基于神经网络的MOSFET器件直流建模技术研究。提出了两种基于神经网络-空间映射技术的MOSFET直流特性建模的方法：第一种方法利用传统的神经网络-空间映射技术对直流特性建模也称之为Neuro-SM模型；第二种方法是将传统神经网络-空间映射技术与先验知识注入及源差分法结合起来也称之为NSM-PKI-D模型。推导了上述两种方法中MOSFET器件跨导和漏导的公式。漏电流特性、跨导和漏导的模拟和测量结果比较证明了我们模型的有效性。
基于神经网络的MOSFET器件大信号建模技术研究。提出了一种将等效电路和神经网络-空间映射建模技术相结合的MOSFET大信号建模方法,该方法不仅保持了模型各部分的物理含义,而且结合了神经网络-空间映射建模技术的优势。为了解决DC/AC色散问题,两个基于神经网络-空间映射的Neuro-SM模型分别用来模拟在直流和射频不同情况下的漏电流。栅长为0.13μm、单栅指的栅宽为5μm、共20个栅指的MOSFET器件的直流及各种不同偏置点下小信号和大信号模拟结果与数据的比较验证了我们模型的有效性。与经验模型的比较进一步验证了我们的模型具有更好的精度。
With the continuous development of wireless communication technology, radio frequency integrated circuit plays an increasingly important role in it. Bulk CMOS technology has become one of the most feasible candidates for building reliable circuits for RF applications. The progress of CMOS technology has made MOSFET transistors show excellent microwave performance. In addition, CMOS technology has such advantages relative to other technologies as mature manufacturing process, low cost, low power, high integration, good integration with high performance digital circuits and high-speed analog circuits, and successful scalability. In order to ensure the circuit performance for the required frequency bands and also to shorten the ratio of time to market, device models are very critical. Artificial neural networks (ANNs) have already been applied to RF and Microwave computer-aided design (CAD) tasks as an unconventional alternative. Neuromodeling is efficient in comparison to conventional modeling methods, such as numerical modeling methods, which could be computationally expensive, or analytical methods, which could be difficult to obtain for new devices, or empirical models, whose range and accuracy could be limited. Furthermore, neural models are easier to develop for new devices than conventional models. Thus, neural network based MOSFET modeling is studied in this thesis. The main content of the thesis is divided into four parts as follows:
Firstly, the characterization and principal modeling method for MOSFET is reviewed. The structure and operation of MOSFET is presented. The characteristic and characterization of MOSFET at high frequency is also studied. Various kinds of modeling methods for MOSFET are summarized, where we focus on neural network based modeling method.
Secondly, neural network based small-signal modeling for MOSFET is studied. The test structure and de-embedding method of MOSFET are presented. Small-signal equivalent circuit and direct parameter extraction method for MOSFET are also studied. Parameters in equivalent circuit are extracted and then optimized, and corresponding bias-dependent empirical model is determined. Bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100MHz-40GHz confirming the validity and effectiveness of our approach.
Thirdly, neural network based DC modeling for MOSFET is studied. Two approaches for modeling DC characteristics for MOSFET based on neuro-space mapping (SM) are proposed. The first approach makes use of classical neuro-SM technology, while the second combines neuro-SM with prior knowledge input and source difference method. The formulas for obtaining the transconductance and output conductance in two approaches are derived. TheⅠ-Ⅴcharacteristics as well as their conductances obtained by the formulas in two approaches are compared to the measured data. Experimental results, which confirm the validity of our approaches, are also presented.
Finally, neural network based large-signal modeling for MOSFET is studied. A large-signal modeling approach based on the combination of equivalent circuit and neuro-space mapping modeling techniques is proposed for MOSFET. In order to account for the dispersion effects, two neuro-space mapping based models are employed to model the drain current at DC and RF conditions respectively. Corresponding training process in our approach is also presented. Good agreement is obtained between the model and data of the DC, S parameter, and harmonic performance for a 0.13μm gate length,5μm gate width per finger and 20 fingers MOSFET over a wide range of bias points, demonstrating the proposed model is valid for DC, small-signal and nonlinear operation. Comparison of DC, S-parameter and harmonic performance between proposed model and empirical model further reveals the better accuracy of the proposed model.
引文
1. Golio Mike. The RF and Microwave Handbook [M]. Boca Raton:CRC Press, 2001.
2. Zolfaghari A and Razavi B. A low-power 2.4-GHz transmitter/receiver CMOS IC[J]. IEEE Journal of Solid-State Circuits,2003,38(2):176-183.
3. Iwai H. CMOS technology-year 2010 and beyond[J]. IEEE Journal of Solid-State Circuits,1999,34(3):357-366.
4. Morifuji E, Momose H S. Ohguro T., Yoshitomi T., Kimijima H., Matsuoka F., Kinugawa M., Katsumata Y. and Iwai H., Future perspective and scaling down roadmap for RF CMOS[A]. Symposium on VLSI Technology[C]. Tokyo:Japan Soc. Appl. Phys,1999.165-166.
5. Chang K M and Wang H P. A new small-signal MOSFET model and parameter extraction method for RF IC's application[J]. Microelectronics Journal,2004, 35(9):749-759.
6. Gao J and Werthof A. Scalable small-signal and noise modeling for deep-submicrometer MOSFETs [J]. IEEE Transactions on Microwave Theory and Techniques,2009,57(4):737-744.
7. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
8. Radio Frequency and Analog/Mixed-signal Technologies for Wireless Communications[R]. International Technology Roadmap for Semiconductors, 2007.
9. Zhang Q J, Gupta K C and Devabhaktuni V K. Artificial neural networks for RF and microwave design-from theory to practice [J]. IEEE Transactions on Microwave Theory and Techniques,2003,51(4):1339-1350.
10. Golio J M. RF and microwave semiconductor device handbook [M]. USA:CRC Press,2003.
11. Steer M B, Bandler J W and Snowden C M. Computer-aided design of RF and microwave circuits and systems [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(3):996-1005.
12. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
13. Angelov G and Hristov M. Spice modeling of MOSFETs in deep submicron [A]. 27th International Spring Seminar on Electronics Technology [C]. Piscataway: IEEE Press,2005.257-262.
14. Chawla B R, Gummel H K and Kozak P. MOTIS-An MOS timing simulator [J]. IEEE Transactions on Circuits and Systems,1975,22(12):901-910.
15. Shima T, Sugawara T, Moriyama S and Yamada H. Three-dimensional table look-up MOSFET model for precise circuit simulation [J]. IEEE Journal of Solid-State Circuits,1982,17(3):449-454.
16. Coughran W M, Grosse E and Rose D J. CAZM-a circuit analyzer with macromodeling [J]. IEEE Transactions on Electron Devices,1983,30(9): 1207-1213.
17. Root D E, Fan S and Meyer J. Technology independent large signal non quasi-static FET models by direct construction from automatically characterized device data [A].21st European Microwave Conference [C]. Tunbridge Wells: Microwave Exhibitions & Publishers,1991.927-932.
18. Angelov I, Rorsman N, Stenarson J, Garcia M and Zirath H. An empirical table-based FET model [J]. IEEE Transactions on Microwave Theory and Techniques,1999,47(12):2350-2357.
19. Bourenkov V, McCarthy K G and Mathewson A. A hybrid table/analytical approach to MOSFET modeling [A]. ICMTS 2003:Proceedings of the 2003 International Conference on Microelectronic Test Structures [C]. Piscataway: IEEE Press,2003.142-147.
1. Cooper B S. Selected applications of neural networks in telecommunication systems:A review, Australian Telecommunication Research [J].1994,28(2):9-29.
2. Chen S, Mulgrew B and Grant P M. A clustering technique for digital-communications channel equalization using radial basis function networks [J]. IEEE Transactions on Neural Networks,1993,4(4):570-579.
3. Chen S, McLaughlin S and Mulgrew B. Complex-valued radial basis function network, Part II:Application to digital communications channel equalization [J]. Signal Processing 36 (1994), no.2,175-188.
4. Hussain B and Kabuka M R. A novel feature recognition neural-network and its application to character-recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(1):98-106.
5. Rowley H A, Baluja S and Kanade T. Neural network-based face detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1): 23-38.
6. Special issue on NN for data mining and knowledge discovery [J]. IEEE Transactions on Neural Networks,2000,11(3).
7. Tsukimoto H. Extracting rules from trained neural networks [J]. IEEE Transactions on Neural Networks,2000,11(2):377-389.
8. Zhang H C and Huang S H. Applications of neural networks in manufacturing-a state-of-the-art survey [J]. International Journal of Production Research,1995, 33(3):705-728.
9. Huang S H and Zhang H C. Artificial neural networks in manufacturing concepts, applications, and perspectives [J]. IEEE Transactions on Components Packaging and Manufacturing Technology Part A,1994,17(2):212-228.
10. Udo G J. Neural networks applications in manufacturing processes [J]. Computers & Industrial Engineering,1992,23(1-4):97-100.
11. Noriega J R and Wang H. A direct adaptive neural-network control for unknown nonlinear systems and its application [J]. IEEE Transactions on Neural Networks, 1998,9(1):27-34.
12. Special issue on NN application in Astronomy and Geosciences [J]. Neural Networks,2003,16(3-4).
13. Cheron G, Draye J P, Bourgeios M and Libert G. A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements [J]. IEEE Transactions on Biomedical Engineering,1996, 43(5):552-558.
14. Zhang Q J, Gupta K. Neural networks for RF and microwave design [M]. Boston: Artech House,2000.
15. Devabhaktuni V K, Xi C G, Wang F and Zhang Q J. Robust training of microwave neural models [J]. International Journal of RF and Microwave Computer-Aided Engineering,2002,12(1):109-124.
16. Wang F and Zhang Q J. Knowledge-based neural models for microwave design [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(12): 2333-2343.
17. Wang F, Devabhaktuni V K and Zhang Q J. A hierarchical neural network approach to the development of a library of neural models for microwave design [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(12): 2391-2403.
18. Antonini G and Orlandi A. Gradient evaluation for neural-networks-based electromagnetic optimization procedures [J]. IEEE Transactions on Microwave Theory and Techniques,2000,48(5):874-876.
19. Zhang Q J, Wang F and Nakhla M. Optimization of high-speed VLSI interconnects:A review [J]. International Journal of Microwave and Millimeter-Wave Computer-Aided Engineering,1997,7(1):83-107.
20. Horng T, Wang C and Alexopoulos N G. Microstrip circuit design using neural networks [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. NewYork:IEEE Press,1993.413-416.
21. Zaabab A H, Zhang Q J and Nakhla M S. Device and circuit-level modeling using neural networks with faster training based on network sparsity [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(10):1696-1704.
22. Watson P M and Gupta K C. EM-ANN models for microstrip vias and interconnects in dataset circuits [J]. IEEE Trans Microwave Theory Tech,1996, 44(12):2495-2503.
23. Watson P M, Gupta K C and Mahajan R L. Applications of knowledge-based artificial neural network modeling to microwave components [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):254-260.
24. Watson P M and Gupta K C. Design and optimization of CPW circuits using EM-ANN models for CPW components [J]. IEEE Trans Microwave Theory Tech, 1997,45(12):2515-2523.
25. Watson P M, Creech G L and Gupta K C. Knowledge based EM-ANN models for the design of wide bandwidth CPW patch/slot antennas [A]. IEEE Antennas and Propagation Society International Symposium Digest [C]. Piscataway:IEEE Press,1999.2588-2591.
26. Creech G L, Paul B, Lesniak C, Jenkins T, Lee R and Calcatera M. Artificial neural networks for accurate microwave CAD applications [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1996. 733-736.
27. Creech G L, Paul B J, Lesniak C D, Jenkins T J and Calcatera M C. Artificial neural networks for fast and accurate EM-CAD of microwave circuits [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(5):794-802.
28. Harkouss Y, Rousset J, Chehade H, Ngoya E, Barataud D and Teyssier J P. Modeling microwave devices and circuits for telecommunications system design [A]. IEEE International Joint Conference on Neural Networks Proceedings [C]. New York:IEEE Press,1998.128-133.
29. Harkouss Y, Rousset J, Chehade H, Ngoya E, Barataud D and Teyssier J P. The use of artificial neural networks in nonlinear microwave devices and circuits modeling:An application to telecommunication system design (invited article) [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999, 9(3):198-215.
30. Zaabab A H, Zhang Q J and Nakhla M. Analysis and optimization of microwave circuits and devices using neural network models [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1994.393-396.
31. Devabhaktuni V K, Xi C G. Wang F. and Zhang Q. J., Robust training of microwave neural models [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press,1999.145-148.
32. Kothapalli G. Artificial neural networks as aids in circuit design [J]. Microelectronics Journal,1995,26(6):569-578.
33. Litovski V B, Radjenovic J I, et al. MOS-transistor modeling using neural network [J]. Electronics Letters,1992,28(18):1766-1768.
34. Creech G L and Zurada J M. Neural network modeling of GaAs IC material and MESFET device characteristics [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):241-253.
35. Rousset J, Harkouss Y, Collantes J M and Campovecchio M. An accurate neural network model of FET for intermodulation and power analysis [A].26th European Microwave Conference [C]. USA:IEEE Press, September 1996.16-19.
36. Zaabab A H, Zhang Q J and Nakhla M. A neural network modeling approach to circuit optimization and statistical design [J]. IEEE Transactions on Microwave Theory and Techniques,1995,43(6):1349-1358.
37. Garcia J A, Puente A T, et al. Modeling MESFETs and HEMTs intermodulation distortion behavior using a generalized radial basis function network [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999, 9(3):261-276.
38. Goasguen S, Hammadi S M and El-Ghazaly S M. A global modeling approach using artificial neural network [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press, June 1999.153-156.
39. Lazaro M, Santamaria I and Pantaleon C. Neural networks for large-and small-signal modeling of MESFET/HEMT transistors [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(6):1587-1593.
40. Giannini F, Leuzzi G, Orengo G and Albertini M. Small-signal and large-signal modeling of active devices using CAD-optimized neural networks [J]. International Journal of RF and Microwave Computer-Aided Engineering,2002, 12(1):71-78.
41. Marinkovic Z D, et al. Artificial neural network applications in improved noise wave modeling of microwave FETs [J]. Microwave and Optical Technology Letters,2008,50(10):2512-2516.
42. Marinkovic Z D, et al. Bias-dependent scalable modeling of microwave FETs based on artificial neural networks [J]. Microwave and Optical Technology Letters,2006,48(10):1932-1936.
43. Devabhaktuni V K, Xi C and Zhang Q J. A neural network approach to the modeling of heterojunction bipolar transistors from S-parameter data [A].28th European Microwave Conference [C]. London:Miller Freeman, October 1998. 306-311.
44. Taher H, Schreurs D and Nauwelaers B. Constitutive relations for nonlinear modeling of Si/SiGe HBTs using an ANN model [J]. International Journal of RF and Microwave Computer-Aided Engineering,2005,15(2):203-209.
45. Taher H., Schreurs D. and Nauwelaers B., Extraction of small signal equivalent circuit model parameters for statistical modeling of HBT using artificial neural [A]. Gallium Arsenide and Other Semiconductor Application Symposium [C]. Piscataway, NJ:IEEE Press,2005.213-216.
46. Markovic V, Prasad S and Stoic A. Noise modeling of heterojunction bipolar transistors using neural network approach [J]. Microwave and Optical Technology Letters,2007,49(4):852-854.
47. Vai M and Prasad S. Qualitative modeling heterojunction bipolar transistors for optimization:A neural network approach [A]. Proc IEEE/Cornell Conf Adv Concepts in High Speed Semiconductor Dev and Circuits [C]. USA:IEEE Press, 1993.219-227.
48. Shirakawa K, Shimiz M, Okubo N and Daido Y. A large-signal characterization of an HEMT using a multilayered neural network [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(9):1630-1633.
49. Shirakawa K, Shimizu M, Okubo N and Daido Y. Structural determination of multilayered large-signal neural-network HEMT model [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(10):1367-1375.
50. Gao J, Zhang L, Xu J and Zhang Q J. Nonlinear HEMT modeling using artificial neural network technique [A]. IEEE MTT-S International Microwave Symposium Digest [C]. USA:IEEE Press,2005.469-472.
51. Davis B, White C, Reece M A, et al. Dynamically configurable pHEMT model using neural networks for CAD [A]. IEEE MTT-S International Microwave Symposium Digest, Philadelphia [C]. Piscataway, NJ:IEEE Press, June 2003. 177-180.
52. Zhang Q J, Wilson G, et al. Ultra fast neural models for analysis of electro/optical interconnects [A].47th Electronic Components and Technology Conference [C]. New York:IEEE Press,1997.1134-1137.
53. Celebi F V and Danisman K. A multi-layer perceptron network model for a quantum-well laser diode [A]. Proceedings of the 2006 International Conference on Computing & Informatics [C]. Piscataway, NJ:IEEE Press, June 2006.1-4.
54. Bakr M H, Bandler J W, Ismail M A, Rayas-Sanchez J E and Zhang Q J. Neural space mapping EM optimization of microwave structures [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press, June 2000.879-882.
55. Kabir H, Wang Y, Yu M and Zhang Q J. High-dimensional neural-network technique and applications to microwave filter modeling [J]. IEEE Transactions on Microwave Theory and Techniques,2010,58(1):145-156.
56. Burrascano P, Dionigi M, Fancelli C and Mongiardo M. A neural network model for CAD and optimization of microwave filters [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1998.13-16.
57. Fedi G, Gaggelli A, et al. Direct-coupled cavity filters design using a hybrid feedforward neural network-finite elements procedure [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):287-296.
58. Bila S, Harkouss Y, et al. An accurate wavelet neural-network-based model for electromagnetic optimization of microwave circuits [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):297-306.
59. Burrascano P, Fiori S and Mongiardo M. A review of artificial neural networks applications in microwave computer-aided design [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):158-174.
60. Vai M, Wu S C, Li B and Prasad S. Creating neural network based microwave circuit models for analysis and synthesis [A]. Asia-Pacific Microwave Conference Proceedings [C]. HongKong:City Univ., Dec.1997.853-856.
61. Harkouss Y, Rousset J, et al. The use of artificial neural networks in nonlinear microwave devices and circuits modeling:An application to telecommunication system design (invited article) [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):198-215.
62. Fang Y H, Yagoub M C E, Wang F and Zhang Q J, A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks [J]. IEEE Transactions on Microwave Theory and Techniques,2000,48(12): 2335-2344.
63. Xu J J, Yagoub M C E, Ding R T and Zhang Q J. Neural-based dynamic modeling of nonlinear microwave circuits [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(12):2769-2780.
64. Washington G. Aperture antenna shape prediction by feedforward neural networks [J]. IEEE Transactions on Antennas and Propagation,1997,45(4):683-688.
65. Mishra R K and Patnaik A. Neural network-based CAD model for the design of square-patch antennas [J], IEEE Transactions on Antennas and Propagation,1998, 46(12):1890-1891.
66. Zooghby A H E, Christodoulou C G and Georgiopoulos M. Neural network-based adaptive beamforming for one-and two-dimensional antenna arrays [J]. IEEE Transactions on Antennas and Propagation,1998,46(12):1891-1893.
67. Lee K, Jhang J and Lin T. An automatically converging scheme based on the neural network and its application in antennas [J]. IEEE Transactions on Antennas and Propagation,2009,53(3):1270-1274.
68. Vakula D and Sarma N. A method for diagnosis of current faults in antenna arrays using neural networks [J]. International Journal of RF and Microwave Computer-Aided Engineering,2009,19(2):270-276.
69. Vai M K and Prasad S. Microwave circuit analysis and design by a massively distributed computing network [J]. IEEE Transactions on Microwave Theory and Techniques,1995,43(5):1087-1094.
70. Kabir H, Wang Y, Yu M and Zhang Q J. Neural network inverse modeling and applications to microwave filter design [J]. IEEE Transactions on Microwave Theory and Techniques,2008,56(4):867-879.
71. Vai M M, Wu S I, Li B and Prasad S. Reverse modeling of microwave circuits with bidirectional neural network models [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(10):1492-1494.
72. Vai M and Prasad S. Neural networks in microwave circuit design-beyond black-box models (invited article) [J], International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):187-197.
73. Jargon J A Gupta K C and DeGroot D C. Applications of artificial neural networks to RF and microwave measurements [J], International Journal of RF and Microwave Computer-Aided Engineering,2002,12(1):3-24.
74. Haykin S, Neural networks:A comprehensive foundation,2nd edition [M]. USA: Prentice-Hall,1999.
75. Widrow B, Lehr M A.30 years of adaptive neural networks:Perceptron, madaline, and back-propagation [J]. Proceedings of the IEEE,1990,78(9): 1415-1442.
76. Minskey M, Papert S. Perceptrons [M]. Cambridge, MA:MIT Press,1969.
77. Cybenko G. Approximation by superpositions of a sigmoidal function [J]. Math Control Signals Syst 2,1989,2(4):303-314.
78. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators [J]. Neural Networks 2,1989,359-366.
79. Park J and Sandberg I W. Universal approximation using radial-basis-function networks [A]. Proceedings Twenty-Eighth Annual Allerton Conference on Communication, Control and Computing [C]. Urbana-Champaign:Univ. Illinois, October 1990.21-30.
80. Park J and Sandberg I W. Universal approximation using radial-basis-function networks [J]. Neural Computation,1991,3(2):246-257.
81. Vogl T P, Mangis J K, Rigler A K, Zink W T and Alkon D L. Accelerating the convergence of the back-propagation method [J]. Biological Cybernetics,1988, 59(4-5):257-263.
82. Goldberg D E. Genetic algorithms in search, optimization and machine learning [M]. MA:Addison-Wesley,1989.
83. Kennedy J and Eberhart R. Particle swarm optimization [A]. IEEE International Conference on Neural Networks Proceedings [C]. New York:IEEE Press,1995. 1942-1948.
84. Dorigo M, Maniezzo V and Colorni A. Ant system:Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics,1996,26(1):29-41.
85. Kirkpatrick S, Gelatt C D and Vecchi M P. Optimization by simulated annealing [J]. Science,1983,220(4598):671-680.
86. Santamaria I, Lazaro M, et al. A nonlinear MESFET model for intermodulation analysis using a generalized radial basis function network [J]. Neurocomputing, 1999,25(1-3):1-18.
87. Lazaro M, Santamaria I, et al. Neural networks for large-and small-signal modeling of MESFET/HEMT transistors [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(6):1587-1593.
88. Schreurs D, Verspecht J, Vandenberghe S and Vandamme E. Straightforward and accurate nonlinear device model parameter-estimation method based on vectorial large-signal measurements [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(10):2315-2319.
89. Watson P M, Gupta K C and Mahajan R L. Development of knowledge based artificial neural network models for microwave components [A]. MTT-S Int Microwave Symp Dig [C]. New York:IEEE Press,1998.9-12.
90. Ladbrooke P H. MMIC Design:GaAs FET's and HEMTs [M]. Boston:Artech House,1989.
91. Bandler J W, Biernacki R M, Chen S H, Grobelny P A and Hemmers R H. Space mapping technique for electromagnetic optimization [J]. IEEE Transactions on Microwave Theory and Techniques,1994,42(12):2536-2544.
92. Bandler J W, Cheng Q S S, Dakroury S A, Mohamed A S, Bakr M H, Madsen K and Sendergaard J. Space mapping:The state of the art [J]. IEEE Trans Microwave Theory Tech,2004,52(1):337-361.
1. Ytterdal T, Cheng Y H, and Fjeldly T A. Device modeling for analog and RF CMOS circuit design [M]. New York:Wiley,2003.
2. Razavi B. Design of analog CMOS and integrated circuits [M]. USA: McGraw-Hill,2001.
3. Abou-Allam E and Manku T. A small-signal MOSFET model for radio frequency IC applications [J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,1997,16(5):437-447.
4. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
5. Jin X D, Ou J, Chen C, et al. An effective gate resistance model for CMOS RF and noise modeling [A]. International Electron Devices Meeting 1998 [C]. Piscataway, NJ:IEEE Press,1998.961-964.
6. Jen S H M, Enz C C, Pehlke D R, Schroter M and Sheu B J. Accurate modeling and parameter extraction for MOS transistors valid up to 10 GHz [J]. IEEE Transactions on Electron Devices,1999,46(11):2217-2227.
7. Enz C C and Cheng Y H. MOS transistor modeling for RF IC design [J]. IEEE Journal of Solid-State Circuits,2000,35(2):186-201.
8. Liu W, Gharpurey R, Chang M C, Erdogan U, Aggarwal R and Mattia J P. RF MOSFET modeling accounting for distributed substrate and channel resistances with emphasis on the BSIM3v3 SPICE model [A]. International Electron Devices Meeting, Technical Digest [C]. New York:IEEE Press,1997.309-312.
9. Cheng Y. RF modeling issues of deep-submicron MOSFETs for circuit design [A]. Proc. of the IEEE International Conference on Solid-State and Integrated Circuit Technology [C]. Piscataway, NJ:IEEE Press,1998.416-419.
10. Ou J J, et al. CMOS RF modeling for GHz communication IC's [A]. Proc. of the VLSI Symposium on Technology [C]. New York:IEEE Press,1998.94-95.
11. Tin S F, et al. Substrate network modeling for CMOS RF circuit simulation [A]. Proc. IEEE Custom Integrated Circuits Conference [C]. Piscataway, NJ:IEEE Press,1999.583-586.
1. Gao J and Werthof A. Direct parameter extraction method for deep submicrometer metal oxide semiconductor field effect transistor small signal equivalent circuit [J]. IET Microwaves Antennas & Propagation,2009, 3(4):564-571.
2. Torres-Torres R and Murphy-Arteaga R. Straightforward determination of small-signal model parameters for bulk RF-MOSFETs [A]. ICCDCS 2004:Fifth International Caracas Conference on Devices, Circuits and Systems [C]. Piscataway, NJ:IEEE Press,2004.14-18.
3. Lovelace D et al. Extracting small signal model parameters of silicon MOSFET transistors [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. New York:IEEE Press, June 1994.865-868.
4. Zhang Q J, Gupta K C, and Devabhaktuni V K. Artificial neural networks for RF and microwave design:From theory to practice [J]. IEEE Trans Microwave Theory Tech,2003,51,1339-1350.
5. Ytterdal T, Cheng Y H, and Fjeldly T A. Device modeling for analog and RF CMOS circuit design [M]. New York:Wiley,2003.
6. Torres-Torres R, Murphy-Arteaga R and Reynoso-Hernandez J A. Analytical model and parameter extraction to account for the pad parasitics in RF-CMOS [J]. IEEE Transactions on Electron Devices,2005,52(7):1335-1342.
7. Wijnen P J V, et al. A new straightforward calibration and correction procedure for 'on wafer' high frequency S-parameter measurements (45MHz-18GHz) [A]. Proceedings of the 1987 Bipolar Circuits and Technology Meeting [C]. New York: IEEE Press,1987.70-73.
8. Koolen M, Geelen J A M and Versleijen M. An improved de-embedding technique for on-wafer high-frequency characterization [A]. Proceedings of the 1991 Bipolar Circuits and Technology Meeting [C]. New York:IEEE Press,1991. 188-191.
9. Bracale A, Ferlet-Cavrois V, Fel N, Pasquet D, Gautier J L, Pelloie J L and Poncharra J D De. A new approach for SOI devices small-signal parameters extraction [J]. Analog Integrated Circuits and Signal Processing,2000,25(2): 157-169.
10. Torres-Torres R, Murphy-Arteaga R S and Decoutere S. MOSFET bias dependent series resistance extraction from RF measurements [J]. Electronics Letters,2003, 39(20):1476-1478.
11. Raskin J P, Gillon R, Chen J, Vanhoenacker-Janvier D and Colinge J P. Accurate SOI MOSFET characterization at microwave frequencies for device performance optimization and analog modeling [J]. IEEE Transactions on Electron Devices, 1998,45(5):1017-1025.
12. Chan Y J, Huang C H, Weng C C and Liew B K. Characteristics of deep-submicrometer MOSFET and its empirical nonlinear RF model [J]. IEEE Trans. Microw. Theory Tech.,1998,46(5):611-615.
13. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
14. Angelov I, Zirath H and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J]. IEEE Trans Microwave Theory Tech,1992,40(12): 2258-2266.
15. Zhang L, Xu J, Yagoub M C E, Ding R T, and Zhang Q J. Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling [J]. IEEE Trans. Microw. Theory Tech.,2005, 53(9):2752-2767.
16. Devabhaktuni V K, Chattaraj B, Yagoub M C E and Zhang Q J. Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping [J]. IEEE Trans Microwave Theory Tech,2003,51(7):1822-1833.
17. MATLAB User's Guide [Z]. Natick, MA:MathWorks,2004.
1. Zhang L, Xu J J, Yagoub M C E, Ding R T and Zhang Q J. Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling [J]. IEEE Trans Microwave Theory Tech,2005, 53(9):2752-2767.
2. Maas S A and Neilson D. Modeling MESFET's for intermodulation analysis of mixers and amplifiers [A]. IEEE MTT-S Int Microwave Symp Dig [C]. USA: IEEE Press,1990.1291-1294.
3. Crosmun A M and Maas S A. Minimization of intermodulation distortion in GaAs-MESFET small-signal amplifiers [J]. IEEE Trans Microwave Theory Tech, 1989,37(9):1411-1417.
4. Pedro J C and Perez J. Accurate simulation of GaAs-MESFET's intermodulation distortion using a new drain-source current model [J]. IEEE Trans Microwave Theory Tech,1994,42(1):25-33.
5. Watson P M and Gupta K C. EM-ANN models for microstrip vias and interconnects in dataset circuits [J]. IEEE Trans Microwave Theory Tech,1996, 44(12):2495-2503.
6. Watson P M, Gupta K C and Mahajan R L. Development of knowledge based artificial neural network models for microwave components [A]. MTT-S Int Microwave Symp Dig [C]. New York:IEEE Press,1998.9-12.
7. Simsek M and Sengor N S. A knowledge-based neuromodeling using space mapping technique:Compound space mapping-based neuromodeling [J]. Int. J. Numer. Model,2008,21 (1-2):133-149.
8. Angelov I, Zirath H and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J]. IEEE Trans Microwave Theory Tech,1992, 40(12):2258-2266.
9. Devabhaktuni V K, Chattaraj B, Yagoub M C E and Zhang Q J. Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping [J]. IEEE Trans Microwave Theory Tech,2003,51(7):1822-1833.
10. MATLAB User's Guide [Z]. Natick, MA:MathWorks,2004.
11. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
1. Biber C E, Schmatz M L, Morf T, Lott U and Bachtold W. A nonlinear microwave MOSFET model for spice simulators [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(5):604-610.
2. hAnnaidh B O and Brazil T J. An accurate nonlinear MOSFET model for intermodulation distortion analysis [J]. IEEE Microwave and Wireless Components Letters,2004,14(7):352-354.
3. Chan Y J, Huang C H, Weng C C and Liew B K. Characteristics of deep-submicrometer MOSFET and its empirical nonlinear RF model [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(5):611-615.
4. Vandamme E P, Schreurs D, Dinther C van, Badenes G and Deferm L. Development of a RF large signal MOSFET model, based on an equivalent circuit, and comparison with the BSIM3v3 compact model [J]. Solid-State Electronics,2002,46(3):353-360.
5. Zhang Q J, Gupta K C and Devabhaktuni V K. Artificial neural networks for RF and microwave design-from theory to practice [J]. IEEE Transactions on Microwave Theory and Techniques,2003,51(4):1339-1350.
6. Toner B and Fusco V F. Large-signal modeling of frequency-dispersion effects in submicron MOSFET devices [J]. Microwave and Optical Technology Letters, 2002,34(6):429-432.
7. Angelov I, Zirath H, and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J], IEEE Trans Microwave Theory Tech,1992, 40(12):2258-2266.
8. Chang K M and Wang H P. A new small-signal MOSFET model and parameter extraction method for RF IC's application [J]. Microelectronics Journal,2004, 35(9):749-759.
9. Gao J and Werthof A. Scalable small-signal and noise modeling for deep-submicrometer MOSFETs [J]. IEEE Trans. Microw. Theory Tech.,2009, 57(4):737-744.
10. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
11. Torres-Torres R and Murphy-Arteaga R. Straightforward determination of small-signal model parameters for bulk RF-MOSFETs [A]. ICCDCS 2004:Fifth International Caracas Conference on Devices, Circuits and Systems [C]. Piscataway, NJ:IEEE Press,2004.14-18.
12. Lovelace D et al. Extracting small signal model parameters of silicon MOSFET transistors [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. New York:IEEE Press, June 1994.865-868.
13. Li S, Han B, Cheng J, Gao J. Neuro-Space Mapping-Based DC Modeling for 130-nm MOSFET [J]. Int J RF and Microwave CAE,2010,20,587-592.
2. Zolfaghari A and Razavi B. A low-power 2.4-GHz transmitter/receiver CMOS IC[J]. IEEE Journal of Solid-State Circuits,2003,38(2):176-183.
3. Iwai H. CMOS technology-year 2010 and beyond[J]. IEEE Journal of Solid-State Circuits,1999,34(3):357-366.
4. Morifuji E, Momose H S. Ohguro T., Yoshitomi T., Kimijima H., Matsuoka F., Kinugawa M., Katsumata Y. and Iwai H., Future perspective and scaling down roadmap for RF CMOS[A]. Symposium on VLSI Technology[C]. Tokyo:Japan Soc. Appl. Phys,1999.165-166.
5. Chang K M and Wang H P. A new small-signal MOSFET model and parameter extraction method for RF IC's application[J]. Microelectronics Journal,2004, 35(9):749-759.
6. Gao J and Werthof A. Scalable small-signal and noise modeling for deep-submicrometer MOSFETs [J]. IEEE Transactions on Microwave Theory and Techniques,2009,57(4):737-744.
7. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
8. Radio Frequency and Analog/Mixed-signal Technologies for Wireless Communications[R]. International Technology Roadmap for Semiconductors, 2007.
9. Zhang Q J, Gupta K C and Devabhaktuni V K. Artificial neural networks for RF and microwave design-from theory to practice [J]. IEEE Transactions on Microwave Theory and Techniques,2003,51(4):1339-1350.
10. Golio J M. RF and microwave semiconductor device handbook [M]. USA:CRC Press,2003.
11. Steer M B, Bandler J W and Snowden C M. Computer-aided design of RF and microwave circuits and systems [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(3):996-1005.
12. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
13. Angelov G and Hristov M. Spice modeling of MOSFETs in deep submicron [A]. 27th International Spring Seminar on Electronics Technology [C]. Piscataway: IEEE Press,2005.257-262.
14. Chawla B R, Gummel H K and Kozak P. MOTIS-An MOS timing simulator [J]. IEEE Transactions on Circuits and Systems,1975,22(12):901-910.
15. Shima T, Sugawara T, Moriyama S and Yamada H. Three-dimensional table look-up MOSFET model for precise circuit simulation [J]. IEEE Journal of Solid-State Circuits,1982,17(3):449-454.
16. Coughran W M, Grosse E and Rose D J. CAZM-a circuit analyzer with macromodeling [J]. IEEE Transactions on Electron Devices,1983,30(9): 1207-1213.
17. Root D E, Fan S and Meyer J. Technology independent large signal non quasi-static FET models by direct construction from automatically characterized device data [A].21st European Microwave Conference [C]. Tunbridge Wells: Microwave Exhibitions & Publishers,1991.927-932.
18. Angelov I, Rorsman N, Stenarson J, Garcia M and Zirath H. An empirical table-based FET model [J]. IEEE Transactions on Microwave Theory and Techniques,1999,47(12):2350-2357.
19. Bourenkov V, McCarthy K G and Mathewson A. A hybrid table/analytical approach to MOSFET modeling [A]. ICMTS 2003:Proceedings of the 2003 International Conference on Microelectronic Test Structures [C]. Piscataway: IEEE Press,2003.142-147.
1. Cooper B S. Selected applications of neural networks in telecommunication systems:A review, Australian Telecommunication Research [J].1994,28(2):9-29.
2. Chen S, Mulgrew B and Grant P M. A clustering technique for digital-communications channel equalization using radial basis function networks [J]. IEEE Transactions on Neural Networks,1993,4(4):570-579.
3. Chen S, McLaughlin S and Mulgrew B. Complex-valued radial basis function network, Part II:Application to digital communications channel equalization [J]. Signal Processing 36 (1994), no.2,175-188.
4. Hussain B and Kabuka M R. A novel feature recognition neural-network and its application to character-recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(1):98-106.
5. Rowley H A, Baluja S and Kanade T. Neural network-based face detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1998,20(1): 23-38.
6. Special issue on NN for data mining and knowledge discovery [J]. IEEE Transactions on Neural Networks,2000,11(3).
7. Tsukimoto H. Extracting rules from trained neural networks [J]. IEEE Transactions on Neural Networks,2000,11(2):377-389.
8. Zhang H C and Huang S H. Applications of neural networks in manufacturing-a state-of-the-art survey [J]. International Journal of Production Research,1995, 33(3):705-728.
9. Huang S H and Zhang H C. Artificial neural networks in manufacturing concepts, applications, and perspectives [J]. IEEE Transactions on Components Packaging and Manufacturing Technology Part A,1994,17(2):212-228.
10. Udo G J. Neural networks applications in manufacturing processes [J]. Computers & Industrial Engineering,1992,23(1-4):97-100.
11. Noriega J R and Wang H. A direct adaptive neural-network control for unknown nonlinear systems and its application [J]. IEEE Transactions on Neural Networks, 1998,9(1):27-34.
12. Special issue on NN application in Astronomy and Geosciences [J]. Neural Networks,2003,16(3-4).
13. Cheron G, Draye J P, Bourgeios M and Libert G. A dynamic neural network identification of electromyography and arm trajectory relationship during complex movements [J]. IEEE Transactions on Biomedical Engineering,1996, 43(5):552-558.
14. Zhang Q J, Gupta K. Neural networks for RF and microwave design [M]. Boston: Artech House,2000.
15. Devabhaktuni V K, Xi C G, Wang F and Zhang Q J. Robust training of microwave neural models [J]. International Journal of RF and Microwave Computer-Aided Engineering,2002,12(1):109-124.
16. Wang F and Zhang Q J. Knowledge-based neural models for microwave design [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(12): 2333-2343.
17. Wang F, Devabhaktuni V K and Zhang Q J. A hierarchical neural network approach to the development of a library of neural models for microwave design [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(12): 2391-2403.
18. Antonini G and Orlandi A. Gradient evaluation for neural-networks-based electromagnetic optimization procedures [J]. IEEE Transactions on Microwave Theory and Techniques,2000,48(5):874-876.
19. Zhang Q J, Wang F and Nakhla M. Optimization of high-speed VLSI interconnects:A review [J]. International Journal of Microwave and Millimeter-Wave Computer-Aided Engineering,1997,7(1):83-107.
20. Horng T, Wang C and Alexopoulos N G. Microstrip circuit design using neural networks [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. NewYork:IEEE Press,1993.413-416.
21. Zaabab A H, Zhang Q J and Nakhla M S. Device and circuit-level modeling using neural networks with faster training based on network sparsity [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(10):1696-1704.
22. Watson P M and Gupta K C. EM-ANN models for microstrip vias and interconnects in dataset circuits [J]. IEEE Trans Microwave Theory Tech,1996, 44(12):2495-2503.
23. Watson P M, Gupta K C and Mahajan R L. Applications of knowledge-based artificial neural network modeling to microwave components [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):254-260.
24. Watson P M and Gupta K C. Design and optimization of CPW circuits using EM-ANN models for CPW components [J]. IEEE Trans Microwave Theory Tech, 1997,45(12):2515-2523.
25. Watson P M, Creech G L and Gupta K C. Knowledge based EM-ANN models for the design of wide bandwidth CPW patch/slot antennas [A]. IEEE Antennas and Propagation Society International Symposium Digest [C]. Piscataway:IEEE Press,1999.2588-2591.
26. Creech G L, Paul B, Lesniak C, Jenkins T, Lee R and Calcatera M. Artificial neural networks for accurate microwave CAD applications [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1996. 733-736.
27. Creech G L, Paul B J, Lesniak C D, Jenkins T J and Calcatera M C. Artificial neural networks for fast and accurate EM-CAD of microwave circuits [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(5):794-802.
28. Harkouss Y, Rousset J, Chehade H, Ngoya E, Barataud D and Teyssier J P. Modeling microwave devices and circuits for telecommunications system design [A]. IEEE International Joint Conference on Neural Networks Proceedings [C]. New York:IEEE Press,1998.128-133.
29. Harkouss Y, Rousset J, Chehade H, Ngoya E, Barataud D and Teyssier J P. The use of artificial neural networks in nonlinear microwave devices and circuits modeling:An application to telecommunication system design (invited article) [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999, 9(3):198-215.
30. Zaabab A H, Zhang Q J and Nakhla M. Analysis and optimization of microwave circuits and devices using neural network models [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1994.393-396.
31. Devabhaktuni V K, Xi C G. Wang F. and Zhang Q. J., Robust training of microwave neural models [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press,1999.145-148.
32. Kothapalli G. Artificial neural networks as aids in circuit design [J]. Microelectronics Journal,1995,26(6):569-578.
33. Litovski V B, Radjenovic J I, et al. MOS-transistor modeling using neural network [J]. Electronics Letters,1992,28(18):1766-1768.
34. Creech G L and Zurada J M. Neural network modeling of GaAs IC material and MESFET device characteristics [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):241-253.
35. Rousset J, Harkouss Y, Collantes J M and Campovecchio M. An accurate neural network model of FET for intermodulation and power analysis [A].26th European Microwave Conference [C]. USA:IEEE Press, September 1996.16-19.
36. Zaabab A H, Zhang Q J and Nakhla M. A neural network modeling approach to circuit optimization and statistical design [J]. IEEE Transactions on Microwave Theory and Techniques,1995,43(6):1349-1358.
37. Garcia J A, Puente A T, et al. Modeling MESFETs and HEMTs intermodulation distortion behavior using a generalized radial basis function network [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999, 9(3):261-276.
38. Goasguen S, Hammadi S M and El-Ghazaly S M. A global modeling approach using artificial neural network [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press, June 1999.153-156.
39. Lazaro M, Santamaria I and Pantaleon C. Neural networks for large-and small-signal modeling of MESFET/HEMT transistors [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(6):1587-1593.
40. Giannini F, Leuzzi G, Orengo G and Albertini M. Small-signal and large-signal modeling of active devices using CAD-optimized neural networks [J]. International Journal of RF and Microwave Computer-Aided Engineering,2002, 12(1):71-78.
41. Marinkovic Z D, et al. Artificial neural network applications in improved noise wave modeling of microwave FETs [J]. Microwave and Optical Technology Letters,2008,50(10):2512-2516.
42. Marinkovic Z D, et al. Bias-dependent scalable modeling of microwave FETs based on artificial neural networks [J]. Microwave and Optical Technology Letters,2006,48(10):1932-1936.
43. Devabhaktuni V K, Xi C and Zhang Q J. A neural network approach to the modeling of heterojunction bipolar transistors from S-parameter data [A].28th European Microwave Conference [C]. London:Miller Freeman, October 1998. 306-311.
44. Taher H, Schreurs D and Nauwelaers B. Constitutive relations for nonlinear modeling of Si/SiGe HBTs using an ANN model [J]. International Journal of RF and Microwave Computer-Aided Engineering,2005,15(2):203-209.
45. Taher H., Schreurs D. and Nauwelaers B., Extraction of small signal equivalent circuit model parameters for statistical modeling of HBT using artificial neural [A]. Gallium Arsenide and Other Semiconductor Application Symposium [C]. Piscataway, NJ:IEEE Press,2005.213-216.
46. Markovic V, Prasad S and Stoic A. Noise modeling of heterojunction bipolar transistors using neural network approach [J]. Microwave and Optical Technology Letters,2007,49(4):852-854.
47. Vai M and Prasad S. Qualitative modeling heterojunction bipolar transistors for optimization:A neural network approach [A]. Proc IEEE/Cornell Conf Adv Concepts in High Speed Semiconductor Dev and Circuits [C]. USA:IEEE Press, 1993.219-227.
48. Shirakawa K, Shimiz M, Okubo N and Daido Y. A large-signal characterization of an HEMT using a multilayered neural network [J]. IEEE Transactions on Microwave Theory and Techniques,1997,45(9):1630-1633.
49. Shirakawa K, Shimizu M, Okubo N and Daido Y. Structural determination of multilayered large-signal neural-network HEMT model [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(10):1367-1375.
50. Gao J, Zhang L, Xu J and Zhang Q J. Nonlinear HEMT modeling using artificial neural network technique [A]. IEEE MTT-S International Microwave Symposium Digest [C]. USA:IEEE Press,2005.469-472.
51. Davis B, White C, Reece M A, et al. Dynamically configurable pHEMT model using neural networks for CAD [A]. IEEE MTT-S International Microwave Symposium Digest, Philadelphia [C]. Piscataway, NJ:IEEE Press, June 2003. 177-180.
52. Zhang Q J, Wilson G, et al. Ultra fast neural models for analysis of electro/optical interconnects [A].47th Electronic Components and Technology Conference [C]. New York:IEEE Press,1997.1134-1137.
53. Celebi F V and Danisman K. A multi-layer perceptron network model for a quantum-well laser diode [A]. Proceedings of the 2006 International Conference on Computing & Informatics [C]. Piscataway, NJ:IEEE Press, June 2006.1-4.
54. Bakr M H, Bandler J W, Ismail M A, Rayas-Sanchez J E and Zhang Q J. Neural space mapping EM optimization of microwave structures [A]. IEEE MTT-S International Microwave Symposium Digest [C]. Piscataway, NJ:IEEE Press, June 2000.879-882.
55. Kabir H, Wang Y, Yu M and Zhang Q J. High-dimensional neural-network technique and applications to microwave filter modeling [J]. IEEE Transactions on Microwave Theory and Techniques,2010,58(1):145-156.
56. Burrascano P, Dionigi M, Fancelli C and Mongiardo M. A neural network model for CAD and optimization of microwave filters [A]. IEEE MTT-S International Microwave Symposium Digest [C]. New York:IEEE Press,1998.13-16.
57. Fedi G, Gaggelli A, et al. Direct-coupled cavity filters design using a hybrid feedforward neural network-finite elements procedure [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):287-296.
58. Bila S, Harkouss Y, et al. An accurate wavelet neural-network-based model for electromagnetic optimization of microwave circuits [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):297-306.
59. Burrascano P, Fiori S and Mongiardo M. A review of artificial neural networks applications in microwave computer-aided design [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):158-174.
60. Vai M, Wu S C, Li B and Prasad S. Creating neural network based microwave circuit models for analysis and synthesis [A]. Asia-Pacific Microwave Conference Proceedings [C]. HongKong:City Univ., Dec.1997.853-856.
61. Harkouss Y, Rousset J, et al. The use of artificial neural networks in nonlinear microwave devices and circuits modeling:An application to telecommunication system design (invited article) [J]. International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):198-215.
62. Fang Y H, Yagoub M C E, Wang F and Zhang Q J, A new macromodeling approach for nonlinear microwave circuits based on recurrent neural networks [J]. IEEE Transactions on Microwave Theory and Techniques,2000,48(12): 2335-2344.
63. Xu J J, Yagoub M C E, Ding R T and Zhang Q J. Neural-based dynamic modeling of nonlinear microwave circuits [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(12):2769-2780.
64. Washington G. Aperture antenna shape prediction by feedforward neural networks [J]. IEEE Transactions on Antennas and Propagation,1997,45(4):683-688.
65. Mishra R K and Patnaik A. Neural network-based CAD model for the design of square-patch antennas [J], IEEE Transactions on Antennas and Propagation,1998, 46(12):1890-1891.
66. Zooghby A H E, Christodoulou C G and Georgiopoulos M. Neural network-based adaptive beamforming for one-and two-dimensional antenna arrays [J]. IEEE Transactions on Antennas and Propagation,1998,46(12):1891-1893.
67. Lee K, Jhang J and Lin T. An automatically converging scheme based on the neural network and its application in antennas [J]. IEEE Transactions on Antennas and Propagation,2009,53(3):1270-1274.
68. Vakula D and Sarma N. A method for diagnosis of current faults in antenna arrays using neural networks [J]. International Journal of RF and Microwave Computer-Aided Engineering,2009,19(2):270-276.
69. Vai M K and Prasad S. Microwave circuit analysis and design by a massively distributed computing network [J]. IEEE Transactions on Microwave Theory and Techniques,1995,43(5):1087-1094.
70. Kabir H, Wang Y, Yu M and Zhang Q J. Neural network inverse modeling and applications to microwave filter design [J]. IEEE Transactions on Microwave Theory and Techniques,2008,56(4):867-879.
71. Vai M M, Wu S I, Li B and Prasad S. Reverse modeling of microwave circuits with bidirectional neural network models [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(10):1492-1494.
72. Vai M and Prasad S. Neural networks in microwave circuit design-beyond black-box models (invited article) [J], International Journal of RF and Microwave Computer-Aided Engineering,1999,9(3):187-197.
73. Jargon J A Gupta K C and DeGroot D C. Applications of artificial neural networks to RF and microwave measurements [J], International Journal of RF and Microwave Computer-Aided Engineering,2002,12(1):3-24.
74. Haykin S, Neural networks:A comprehensive foundation,2nd edition [M]. USA: Prentice-Hall,1999.
75. Widrow B, Lehr M A.30 years of adaptive neural networks:Perceptron, madaline, and back-propagation [J]. Proceedings of the IEEE,1990,78(9): 1415-1442.
76. Minskey M, Papert S. Perceptrons [M]. Cambridge, MA:MIT Press,1969.
77. Cybenko G. Approximation by superpositions of a sigmoidal function [J]. Math Control Signals Syst 2,1989,2(4):303-314.
78. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators [J]. Neural Networks 2,1989,359-366.
79. Park J and Sandberg I W. Universal approximation using radial-basis-function networks [A]. Proceedings Twenty-Eighth Annual Allerton Conference on Communication, Control and Computing [C]. Urbana-Champaign:Univ. Illinois, October 1990.21-30.
80. Park J and Sandberg I W. Universal approximation using radial-basis-function networks [J]. Neural Computation,1991,3(2):246-257.
81. Vogl T P, Mangis J K, Rigler A K, Zink W T and Alkon D L. Accelerating the convergence of the back-propagation method [J]. Biological Cybernetics,1988, 59(4-5):257-263.
82. Goldberg D E. Genetic algorithms in search, optimization and machine learning [M]. MA:Addison-Wesley,1989.
83. Kennedy J and Eberhart R. Particle swarm optimization [A]. IEEE International Conference on Neural Networks Proceedings [C]. New York:IEEE Press,1995. 1942-1948.
84. Dorigo M, Maniezzo V and Colorni A. Ant system:Optimization by a colony of cooperating agents [J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics,1996,26(1):29-41.
85. Kirkpatrick S, Gelatt C D and Vecchi M P. Optimization by simulated annealing [J]. Science,1983,220(4598):671-680.
86. Santamaria I, Lazaro M, et al. A nonlinear MESFET model for intermodulation analysis using a generalized radial basis function network [J]. Neurocomputing, 1999,25(1-3):1-18.
87. Lazaro M, Santamaria I, et al. Neural networks for large-and small-signal modeling of MESFET/HEMT transistors [J]. IEEE Transactions on Instrumentation and Measurement,2001,50(6):1587-1593.
88. Schreurs D, Verspecht J, Vandenberghe S and Vandamme E. Straightforward and accurate nonlinear device model parameter-estimation method based on vectorial large-signal measurements [J]. IEEE Transactions on Microwave Theory and Techniques,2002,50(10):2315-2319.
89. Watson P M, Gupta K C and Mahajan R L. Development of knowledge based artificial neural network models for microwave components [A]. MTT-S Int Microwave Symp Dig [C]. New York:IEEE Press,1998.9-12.
90. Ladbrooke P H. MMIC Design:GaAs FET's and HEMTs [M]. Boston:Artech House,1989.
91. Bandler J W, Biernacki R M, Chen S H, Grobelny P A and Hemmers R H. Space mapping technique for electromagnetic optimization [J]. IEEE Transactions on Microwave Theory and Techniques,1994,42(12):2536-2544.
92. Bandler J W, Cheng Q S S, Dakroury S A, Mohamed A S, Bakr M H, Madsen K and Sendergaard J. Space mapping:The state of the art [J]. IEEE Trans Microwave Theory Tech,2004,52(1):337-361.
1. Ytterdal T, Cheng Y H, and Fjeldly T A. Device modeling for analog and RF CMOS circuit design [M]. New York:Wiley,2003.
2. Razavi B. Design of analog CMOS and integrated circuits [M]. USA: McGraw-Hill,2001.
3. Abou-Allam E and Manku T. A small-signal MOSFET model for radio frequency IC applications [J]. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems,1997,16(5):437-447.
4. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
5. Jin X D, Ou J, Chen C, et al. An effective gate resistance model for CMOS RF and noise modeling [A]. International Electron Devices Meeting 1998 [C]. Piscataway, NJ:IEEE Press,1998.961-964.
6. Jen S H M, Enz C C, Pehlke D R, Schroter M and Sheu B J. Accurate modeling and parameter extraction for MOS transistors valid up to 10 GHz [J]. IEEE Transactions on Electron Devices,1999,46(11):2217-2227.
7. Enz C C and Cheng Y H. MOS transistor modeling for RF IC design [J]. IEEE Journal of Solid-State Circuits,2000,35(2):186-201.
8. Liu W, Gharpurey R, Chang M C, Erdogan U, Aggarwal R and Mattia J P. RF MOSFET modeling accounting for distributed substrate and channel resistances with emphasis on the BSIM3v3 SPICE model [A]. International Electron Devices Meeting, Technical Digest [C]. New York:IEEE Press,1997.309-312.
9. Cheng Y. RF modeling issues of deep-submicron MOSFETs for circuit design [A]. Proc. of the IEEE International Conference on Solid-State and Integrated Circuit Technology [C]. Piscataway, NJ:IEEE Press,1998.416-419.
10. Ou J J, et al. CMOS RF modeling for GHz communication IC's [A]. Proc. of the VLSI Symposium on Technology [C]. New York:IEEE Press,1998.94-95.
11. Tin S F, et al. Substrate network modeling for CMOS RF circuit simulation [A]. Proc. IEEE Custom Integrated Circuits Conference [C]. Piscataway, NJ:IEEE Press,1999.583-586.
1. Gao J and Werthof A. Direct parameter extraction method for deep submicrometer metal oxide semiconductor field effect transistor small signal equivalent circuit [J]. IET Microwaves Antennas & Propagation,2009, 3(4):564-571.
2. Torres-Torres R and Murphy-Arteaga R. Straightforward determination of small-signal model parameters for bulk RF-MOSFETs [A]. ICCDCS 2004:Fifth International Caracas Conference on Devices, Circuits and Systems [C]. Piscataway, NJ:IEEE Press,2004.14-18.
3. Lovelace D et al. Extracting small signal model parameters of silicon MOSFET transistors [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. New York:IEEE Press, June 1994.865-868.
4. Zhang Q J, Gupta K C, and Devabhaktuni V K. Artificial neural networks for RF and microwave design:From theory to practice [J]. IEEE Trans Microwave Theory Tech,2003,51,1339-1350.
5. Ytterdal T, Cheng Y H, and Fjeldly T A. Device modeling for analog and RF CMOS circuit design [M]. New York:Wiley,2003.
6. Torres-Torres R, Murphy-Arteaga R and Reynoso-Hernandez J A. Analytical model and parameter extraction to account for the pad parasitics in RF-CMOS [J]. IEEE Transactions on Electron Devices,2005,52(7):1335-1342.
7. Wijnen P J V, et al. A new straightforward calibration and correction procedure for 'on wafer' high frequency S-parameter measurements (45MHz-18GHz) [A]. Proceedings of the 1987 Bipolar Circuits and Technology Meeting [C]. New York: IEEE Press,1987.70-73.
8. Koolen M, Geelen J A M and Versleijen M. An improved de-embedding technique for on-wafer high-frequency characterization [A]. Proceedings of the 1991 Bipolar Circuits and Technology Meeting [C]. New York:IEEE Press,1991. 188-191.
9. Bracale A, Ferlet-Cavrois V, Fel N, Pasquet D, Gautier J L, Pelloie J L and Poncharra J D De. A new approach for SOI devices small-signal parameters extraction [J]. Analog Integrated Circuits and Signal Processing,2000,25(2): 157-169.
10. Torres-Torres R, Murphy-Arteaga R S and Decoutere S. MOSFET bias dependent series resistance extraction from RF measurements [J]. Electronics Letters,2003, 39(20):1476-1478.
11. Raskin J P, Gillon R, Chen J, Vanhoenacker-Janvier D and Colinge J P. Accurate SOI MOSFET characterization at microwave frequencies for device performance optimization and analog modeling [J]. IEEE Transactions on Electron Devices, 1998,45(5):1017-1025.
12. Chan Y J, Huang C H, Weng C C and Liew B K. Characteristics of deep-submicrometer MOSFET and its empirical nonlinear RF model [J]. IEEE Trans. Microw. Theory Tech.,1998,46(5):611-615.
13. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
14. Angelov I, Zirath H and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J]. IEEE Trans Microwave Theory Tech,1992,40(12): 2258-2266.
15. Zhang L, Xu J, Yagoub M C E, Ding R T, and Zhang Q J. Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling [J]. IEEE Trans. Microw. Theory Tech.,2005, 53(9):2752-2767.
16. Devabhaktuni V K, Chattaraj B, Yagoub M C E and Zhang Q J. Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping [J]. IEEE Trans Microwave Theory Tech,2003,51(7):1822-1833.
17. MATLAB User's Guide [Z]. Natick, MA:MathWorks,2004.
1. Zhang L, Xu J J, Yagoub M C E, Ding R T and Zhang Q J. Efficient analytical formulation and sensitivity analysis of neuro-space mapping for nonlinear microwave device modeling [J]. IEEE Trans Microwave Theory Tech,2005, 53(9):2752-2767.
2. Maas S A and Neilson D. Modeling MESFET's for intermodulation analysis of mixers and amplifiers [A]. IEEE MTT-S Int Microwave Symp Dig [C]. USA: IEEE Press,1990.1291-1294.
3. Crosmun A M and Maas S A. Minimization of intermodulation distortion in GaAs-MESFET small-signal amplifiers [J]. IEEE Trans Microwave Theory Tech, 1989,37(9):1411-1417.
4. Pedro J C and Perez J. Accurate simulation of GaAs-MESFET's intermodulation distortion using a new drain-source current model [J]. IEEE Trans Microwave Theory Tech,1994,42(1):25-33.
5. Watson P M and Gupta K C. EM-ANN models for microstrip vias and interconnects in dataset circuits [J]. IEEE Trans Microwave Theory Tech,1996, 44(12):2495-2503.
6. Watson P M, Gupta K C and Mahajan R L. Development of knowledge based artificial neural network models for microwave components [A]. MTT-S Int Microwave Symp Dig [C]. New York:IEEE Press,1998.9-12.
7. Simsek M and Sengor N S. A knowledge-based neuromodeling using space mapping technique:Compound space mapping-based neuromodeling [J]. Int. J. Numer. Model,2008,21 (1-2):133-149.
8. Angelov I, Zirath H and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J]. IEEE Trans Microwave Theory Tech,1992, 40(12):2258-2266.
9. Devabhaktuni V K, Chattaraj B, Yagoub M C E and Zhang Q J. Advanced microwave modeling framework exploiting automatic model generation, knowledge neural networks, and space mapping [J]. IEEE Trans Microwave Theory Tech,2003,51(7):1822-1833.
10. MATLAB User's Guide [Z]. Natick, MA:MathWorks,2004.
11. Gao J. RF and microwave modeling and measurement techniques for field effect transistors [M]. USA:SciTech Publishing,2009.
1. Biber C E, Schmatz M L, Morf T, Lott U and Bachtold W. A nonlinear microwave MOSFET model for spice simulators [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(5):604-610.
2. hAnnaidh B O and Brazil T J. An accurate nonlinear MOSFET model for intermodulation distortion analysis [J]. IEEE Microwave and Wireless Components Letters,2004,14(7):352-354.
3. Chan Y J, Huang C H, Weng C C and Liew B K. Characteristics of deep-submicrometer MOSFET and its empirical nonlinear RF model [J]. IEEE Transactions on Microwave Theory and Techniques,1998,46(5):611-615.
4. Vandamme E P, Schreurs D, Dinther C van, Badenes G and Deferm L. Development of a RF large signal MOSFET model, based on an equivalent circuit, and comparison with the BSIM3v3 compact model [J]. Solid-State Electronics,2002,46(3):353-360.
5. Zhang Q J, Gupta K C and Devabhaktuni V K. Artificial neural networks for RF and microwave design-from theory to practice [J]. IEEE Transactions on Microwave Theory and Techniques,2003,51(4):1339-1350.
6. Toner B and Fusco V F. Large-signal modeling of frequency-dispersion effects in submicron MOSFET devices [J]. Microwave and Optical Technology Letters, 2002,34(6):429-432.
7. Angelov I, Zirath H, and Rorsman N. A new empirical nonlinear model for HEMT and MESFET devices [J], IEEE Trans Microwave Theory Tech,1992, 40(12):2258-2266.
8. Chang K M and Wang H P. A new small-signal MOSFET model and parameter extraction method for RF IC's application [J]. Microelectronics Journal,2004, 35(9):749-759.
9. Gao J and Werthof A. Scalable small-signal and noise modeling for deep-submicrometer MOSFETs [J]. IEEE Trans. Microw. Theory Tech.,2009, 57(4):737-744.
10. Cheng Y H, Deen M J and Chen C H. MOSFET modeling for RF IC design [J]. IEEE Transactions on Electron Devices,2005,52(7):1286-1303.
11. Torres-Torres R and Murphy-Arteaga R. Straightforward determination of small-signal model parameters for bulk RF-MOSFETs [A]. ICCDCS 2004:Fifth International Caracas Conference on Devices, Circuits and Systems [C]. Piscataway, NJ:IEEE Press,2004.14-18.
12. Lovelace D et al. Extracting small signal model parameters of silicon MOSFET transistors [A]. IEEE MTT-S Int. Microwave Symp. Dig. [C]. New York:IEEE Press, June 1994.865-868.
13. Li S, Han B, Cheng J, Gao J. Neuro-Space Mapping-Based DC Modeling for 130-nm MOSFET [J]. Int J RF and Microwave CAE,2010,20,587-592.