Hung, Peter C. and McLoone, Sean F. and Sainchez, Magdalena and Farrell, Ronan
Inferential estimation of high frequency LNA gain performance using machine learning techniques.
Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing.
Functional testing of radio frequency integrated
circuits is a challenging task and one that is becoming
an increasingly expensive aspect of circuit
manufacture. Due to the difficulties with bringing high
frequency signals off-chip, current automated test
equipment (ATE) technologies are approaching the
limits of their operating capabilities as circuits are
pushed to operate at higher and higher frequencies.
This paper explores the possibility of extending the
operating range of existing ATEs by using machine
learning techniques to infer high frequency circuit
performance from more accessible lower frequency
and DC measurements. Results from a simulation
study conducted on a low noise amplifier (LNA) circuit
operating at 2.4 GHz demonstrate that the proposed
approach has the potential to substantially increase
the operating bandwidth ofATE.
||Automatic test equipment; integrated circuit manufacture; integrated circuit testing; learning (artificial intelligence); low noise amplifiers; radiofrequency integrated circuits.
||Faculty of Science and Engineering > Electronic Engineering
||08 May 2012 13:36
|Journal or Publication Title:
||Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing
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