Perspectives on Test Data Mining from Industrial Experience

Author

Chen, He Henry

Author_Institution

Design Technol. Dept., MediaTek Inc., Hsinchu, Taiwan

fYear

2014

fDate

16-19 Nov. 2014

Firstpage

242

Lastpage

247

Abstract

This paper offers some perspectives on the practice of data mining based on recent experimental research work to establish a link between system-level failures and structural scan test patterns. Beyond the obvious goal to obtain accurate results, knowledge discovery and data insights deserve equal if not higher emphasis. Domain knowledge plays a crucial role in guiding the use of multiple machine learning tools through the fog of data noise towards usable results. A description of data analysis performed on a 28-nm 1.2-GHz quad-core mobile processor serves to illustrate the perspectives.

Keywords

boundary scan testing; data analysis; data mining; learning (artificial intelligence); microprocessor chips; data analysis; data mining; frequency 1.2 GHz; machine learning tools; quad-core mobile processor; size 28 nm; structural scan test; system-level failures; Data collection; Data mining; Histograms; Production; Radio frequency; Stress; System-on-chip; SOMAC methodology; data mining; higher-than-at-speed test; low-voltage test; machine learning; on-chip-clock patterns; structural scan test; system-level test;

fLanguage

English

Publisher

ieee

Conference_Titel

Test Symposium (ATS), 2014 IEEE 23rd Asian

Conference_Location

Hangzhou

ISSN

1081-7735

Type

conf

DOI

10.1109/ATS.2014.52

Filename

6979107