A GA-Based X-Filling for reducing launch switching activity toward specific objectives in at-speed scan testing

Yuta Yamato, Xiaoqing Wen, Kohei Miyase, Hiroshi Furukawa, Seiji Kajihara

Research output: Contribution to journalArticle

Abstract

Power-aware X-filling is a preferable approach to avoiding IR-drop-induced yield loss in at-speed scan testing. However, the ability of previous X-filling methods to reduce launch switching activity may be unsatisfactory, due to low effect (insufficient and global-only reduction) and/or low scalability (long CPU time). This paper addresses this reduction quality problem with a novel GA (Genetic Algorithm) based X-filling method, called GA-fill. Its goals are (1) to achieve both effectiveness and scalability in a more balanced manner and (2) to make the reduction effect of launch switching activity more concentrated on critical areas that have higher impact on IR-drop-induced yield loss. Evaluation experiments are being conducted on both benchmark and industrial circuits, and the results have demonstrated the usefulness of GA-fill.

Original languageEnglish
Pages (from-to)833-840
Number of pages8
JournalIEICE Transactions on Information and Systems
VolumeE94-D
Issue number4
DOIs
Publication statusPublished - Apr 2011

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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