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Inferential estimation of high frequency LNA gain performance using machine learning techniques
Hung, Peter C.; McLoone, Sean F.; Sainchez, Magdalena; Farrell, Ronan
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.
Keyword(s): Electronic Engineering; Automatic test equipment; integrated circuit manufacture; integrated circuit testing; learning (artificial intelligence); low noise amplifiers; radiofrequency integrated circuits.
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Hung, Peter C. and McLoone, Sean F. and Sainchez, Magdalena and Farrell, Ronan (2007) Inferential estimation of high frequency LNA gain performance using machine learning techniques. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing. pp. 276-281. ISSN 1551-2541
Publisher(s): IEEE
File Format(s): application/pdf
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First Indexed: 2020-01-31 06:01:16 Last Updated: 2020-04-02 07:36:31