Identifying good-dice-in-bad-neighborhoods using artificial neural networks

Cheng Hao Yang, Chia Heng Yen, Ting Rui Wang, Chun Teng Chen, Mason Chern, Ying Yen Chen, Jih Nung Lee, Shu Yi Kao, Kai-Chiang Wu, Chia-Tso Chao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations


GDBN (good die in bad neighborhood) methodology has been regarded as an effective technique for reducing DPPM (defect parts per million), by identifying and rejecting suspicious dice even though they test good. Instead of examining eight immediate neighbors or exploiting simple linear regression, in this paper we propose to employ a window of larger size for broad-sighted recognition of neighborhood, and make best use of the larger window for accurate prediction of the suspicious level for any given die. The proposed methodology is realized by using an artificial neural network (NN), and is a breakthrough of NN-based work for solving the problem of GDBN. Various experiments on two sets of data clearly reveal the superiority of our NN-based methodology over other existing methods. Besides reducing DPPM, our methodology is able to achieve 1. 5X-2X better reduction in the cost for return merchandise authorization (RMA).

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 39th VLSI Test Symposium, VTS 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665419499
StatePublished - 25 Apr 2021
Event39th IEEE VLSI Test Symposium, VTS 2021 - San Diego, United States
Duration: 26 Apr 202128 Apr 2021

Publication series

NameProceedings of the IEEE VLSI Test Symposium


Conference39th IEEE VLSI Test Symposium, VTS 2021
Country/TerritoryUnited States
CitySan Diego


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