On the use of causal feature selection in the context of machine-learning indirect test
M. J. Barragan1, G. Leger2, F. Cilici1,3, E. Lauga-Larroze3, S. Bourdel3 and S. Mir1
1Univ. Grenoble Alpes, CNRS, Grenoble INP1, Grenoble, France
2Instituto de Microlectrónica de Sevilla, CSIC-Universidad de Sevilla, Seville, Spain
3Univ. Grenoble Alpes, RFICLab, Grenoble, France
ABSTRACT
The test of analog, mixed-signal and RF (AMSRF) circuits is still considered as a matter of human creativity, and although many attempts have been made towards their automation, no accepted and complete solution is yet available. Indeed, capturing the design knowledge of an experienced analog designer is one of the key challenges faced by the Electronic Design Automation (EDA) community. In this paper we explore the use of causal inference tools in the context of AMS-RF design and test with the goal of defining a methodology for uncovering the root causes of performance variation in these systems. We believe that such an analysis can be a promising first step for future EDA algorithms for AMS-RF systems.
