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Authors: Peter C. Hung ; Seán F. McLoone ; Magdalena Sánchez ; Ronan Farrell and Guoyan Zhang

Affiliation: Institute of Microelectronics and Wireless Systems, National University of Ireland Maynooth, Ireland

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 off-chip. One possible strategy for circumventing these difficulties is to attempt to predict the high frequency performance measures using measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of machine learning based classification techniques at predicting the gain of the amplifier, a key performance parameter, using such an approach. An indirect artificial neural network (ANN) and direct support vector machine (SVM) classification strategy are considered. Simulations show promising results with both methods, with SVMs outperforming ANNs for the more demanding classification scenarios.

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Paper citation in several formats:
C. Hung, P.; F. McLoone, S.; Sánchez, M.; Farrell, R. and Zhang, G. (2007). Direct and Indirect Classification of High-Frequency LNA Performance using Machine Learning Techniques. In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP; ISBN 978-972-8865-86-3, SciTePress, pages 66-75. DOI: 10.5220/0001634500660075

@conference{anniip07,
author={Peter {C. Hung}. and Seán {F. McLoone}. and Magdalena Sánchez. and Ronan Farrell. and Guoyan Zhang.},
title={Direct and Indirect Classification of High-Frequency LNA Performance using Machine Learning Techniques},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP},
year={2007},
pages={66-75},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001634500660075},
isbn={978-972-8865-86-3},
}

TY - CONF

JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing (ICINCO 2007) - ANNIIP
TI - Direct and Indirect Classification of High-Frequency LNA Performance using Machine Learning Techniques
SN - 978-972-8865-86-3
AU - C. Hung, P.
AU - F. McLoone, S.
AU - Sánchez, M.
AU - Farrell, R.
AU - Zhang, G.
PY - 2007
SP - 66
EP - 75
DO - 10.5220/0001634500660075
PB - SciTePress

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