DIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS

Authors

  • Peter C. Hung
  • Seán F. McLoone
  • Ronan Farrell

DOI:

https://doi.org/10.47839/ijc.8.1.653

Keywords:

LNA, Functional testing, Classification, Support Vector Machines, Multilayer Perceptrons.

Abstract

The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip. One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter. An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP classifiers marginally outperforming SVMs.

References

J. Ferrario, R. Wolf, H. Ding, Moving from mixed signal to RF test hardware development. IEEE Int. Test Conference (2001) pp. 948–956.

W. Y. Lau, Measurement challenges for on-wafer RF-SOC test. 27th Annual IEEE/SEMI Int. Elect. Manufact. Tech. Symp. (2002) pp. 353–359.

M. Negreiros, L. Carro, A. Susin, Low cost on-line testing of RF circuits. 10th IEEE Int. On-Line Testing Symp. (2004) pp. 73–78.

D. C. Doskocil, Advanced RF built in test. AUTOTESTCON '92 IEEE Sys. Readiness Tech. Conf. (1992) pp. 213–217.

M. E. Goff, C. A. Barratt, DC to 40 GHz MMIC power sensor. Gallium Arsenide IC Symp. (1990) pp. 105–108.

S. Bhattacharya, A. Chatterjee, Use of embedded sensors for built-in-test RF circuits, IEEE Int. Test Conf. (2004) pp. 801 – 809.

S. S. Akbay, A. Chatterjee, Feature extraction based built-in alternate test of RF components using a noise reference, 22nd IEEE VLSI Test Symp. (2004) pp. 273 – 278.

Mentor Graphics Corporation, Eldo User Manual (2005).

V. Vapnik, A. Lerner, Pattern recognition using generalised portrait method. Automation and Remote Control 24 (1963).

M.A. Hearst, SVMs – a practical consequence of learning theory. IEEE Intelligent Sys. (1998) pp. 18–21.

C. J. Burges, A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 (1998) pp. 121–167.

C. Cortes, V. Vapnik, Support vector networks. Machine Learning 20 (1995) pp. 273–297

S. Haykin, Neural Networks: A comprehensive foundation. 2nd edn. Prentice Hall, New Jersey (1998).

S. McLoone, M. Brown, G. Irwin, G. Lightbody, A hybrid linear/nonlinear training algorithm for feedforward neural networks. IEEE Trans. on Neural Networks 9 (1998) pp. 669-684.

J. Sjoberg, L. Ljung, Overtraining, regularization, and searching for minimum with application to neural networks. Int. J. Control 62 (1995) pp. 1391-1407.

C.-C. Chang, C.-J. Lin, LibSVM: a library for support vector machines (2001). Software available at http://www.csie.ntu.edu.tw/~cjlin/-libsvm.

S. V. N. Vishwanathan, A. J. Smola, M. N. Murty, SimpleSVM. Proc. 20th Int. Conf. Machine Learning (2003).

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Published

2014-08-01

How to Cite

Hung, P. C., McLoone, S. F., & Farrell, R. (2014). DIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS. International Journal of Computing, 8(1), 24-31. https://doi.org/10.47839/ijc.8.1.653

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Articles