On the use of causal feature selection in the context of machine-learning indirect test

被引:0
作者
Barragan, M. J. [1 ]
Leger, G. [2 ]
Cilici, F. [1 ,3 ]
Lauga-Larroze, E. [3 ]
Bourdel, S. [3 ]
Mir, S. [1 ]
机构
[1] Univ Grenoble Alpes, CNRS, Grenoble INP, TIMA,Inst Engn, F-38000 Grenoble, France
[2] Univ Seville, CSIC, Inst Microlectron Sevilla, Seville 41092, Spain
[3] Univ Grenoble Alpes, RFICLab, F-38000 Grenoble, France
来源
2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE) | 2019年
关键词
ALTERNATE TEST;
D O I
10.23919/date.2019.8714798
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The test of analog, mixed-signal and RF (AMS-RF) 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.
引用
收藏
页码:276 / 279
页数:4
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