Hung, Peter and McLoone, Sean F. and Sanchez, Magdalena and Farrell, Ronan and Zhang, Guoyan (2007) 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.

Download (266kB)

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.

Item Type:	Conference or Workshop Item (Paper)
Keywords:	Direct and Indirect Classification of High-Frequency LNA Performance; Machine Learning Techniques;
Academic Unit:	Faculty of Science and Engineering > Electronic Engineering
Item ID:	1338
Depositing User:	Ronan Farrell
Date Deposited:	26 May 2009 09:01
Refereed:	Yes
URI:
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

Repository Staff Only(login required)

Item control page

Downloads

Origin of downloads