Application of Convolutional Neural Networks to Regenerate Deterministic Test Patterns for BIST

被引：0

作者：

Inamoto, Tsutomu ^{[1
]}

Higami, Yoshinobu ^{[1
]}

机构：

[1] Ehime Univ, Grad Sch Sci & Engn, Matsuyama, Ehime, Japan

来源：

2019 34TH INTERNATIONAL TECHNICAL CONFERENCE ON CIRCUITS/SYSTEMS, COMPUTERS AND COMMUNICATIONS (ITC-CSCC 2019) | 2019年

关键词：

combinational circuit; fault detection; logic BIST; test pattern; artificial neural network;

D O I：

10.1109/itc-cscc.2019.8793374

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This study displays preliminary results on a simple technique to improve fault coverages in the BIST technology. The technique assumes that a circuit which implements an ANN can be used with the target circuit, and effective test patterns are given beforehand. Such ANN circuit is utilized as a degraded memory which approximately regenerates given test patterns. In computational illustrations, fault coverages of test patterns by the technique are calculated on c7552 of the ISCAS'85 benchmark.

引用

页码：114 / 115

页数：2

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