Inferential estimation of high frequency LNA gain performance using machine learning techniques

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 of ATE.