...
首页> 外文期刊>Computers and Electrical Engineering >Test data compression using Lingering Component Reduction technique for system-on-a-chip applications
【24h】

Test data compression using Lingering Component Reduction technique for system-on-a-chip applications

机译:测试数据压缩,使用挥之不去的组件减少技术进行系统芯片应用

获取原文
获取原文并翻译 | 示例
           

摘要

As technology, processes scale up, and design complexities grow, system-on-chip integration continues to rise rapidly. According to these trends, increasing test data volume is one of the largest challenges in the testing industry. In this work, we present a new test data compression method based on a stored set called Lingering Component Reduction (LCR) Code. The test set contains a large number of don't- care that can be exploited to improve the experimental data compression. In this method, a reference pattern is organized, and an adaptability of input pattern with reference pattern is reduced. The Lingering Component estimation based on test pattern contains more don't-care conditions that can be actively reduced to improve the test data compression on the Intellectual Property cores. The simulation results show the performance of experimental data compression ratio and testing time parameters. From the analysis of simulation results, it is proved that the proposed LCR code enhances a compression ratio and reduce the test time follows the International Symposium on Circuits and Systems'89. The comparative results of several benchmark circuits in maximum cases and compared the outputs of the many previous works without a substantial load on the hardware. (C) 2018 Elsevier Ltd. All rights reserved.
机译:随着技术,流程扩展,设计复杂性成长,片上整合继续迅速上升。根据这些趋势,越来越多的测试数据量是测试行业中最大的挑战之一。在这项工作中,我们提出了一种基于存储的集合的新测试数据压缩方法,称为挥之不去的组件减少(LCR)代码。测试集包含了大量的不小心,可以利用以改善实验数据压缩。在该方法中,组织了参考图案,并且减少了具有参考图案的输入图案的适应性。基于测试模式的挥之不去的组件估计包含可以积极地减少的更多不关心条件,以改善知识产权核心的测试数据压缩。仿真结果显示了实验数据压缩比和测试时间参数的性能。从仿真结果分析中,证明所提出的LCR码增强了压缩比率,减少了测试时间遵循电路和系统的国际研讨会。在最大案例中多个基准电路的比较结果,并比较了许多先前作品的输出而无需大量负载。 (c)2018年elestvier有限公司保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号