Hung, Peter and McLoone, S. and Sanchez, Magdalena and Farrell, Ronan and Zhang, Guoyan
Direct and Indirect Classification of High-Frequency
LNA Performance using Machine Learning Techniques.
In: ICINCO 2007, International Conference on Information in Control, Automation and Robotics.
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
Conference or Workshop Item
||Direct and Indirect Classification of High-Frequency
LNA Performance; Machine Learning Techniques;
||Faculty of Science and Engineering > Electronic Engineering
Dr. Ronan Farrell
||26 May 2009 09:01
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