A prediction method of test coverage loss under multi-input with unknown

YING Jian-feng; LIANG Hua-guo; JIANG Yue; JIANG Cui-yun; LI Dan-qing; HUANG Zheng-feng

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A prediction method of test coverage loss under multi-input with unknown

更新时间：2026-09-04

- A prediction method of test coverage loss under multi-input with unknown
- Vol. 37, Issue 4, Pages: 12-18(2020)
- 作者机构：
  
  1. 合肥工业大学电子科学与应用物理学院,合肥,230009
  2. 合肥工业大学数学学院,安徽,合肥,23009
- 作者简介：
- 基金信息：
- DOI：
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- Published：2020
- 稿件说明：
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应健锋, 梁华国, 江悦, 蒋翠云, 李丹青, 黄正峰. 一种多X值输入下测试覆盖率损失的预测方法[J]. 微电子学与计算机, 2020, 37(4): 12-18.

YING Jian-feng, LIANG Hua-guo, JIANG Yue, et al. A prediction method of test coverage loss under multi-input with unknown[J]. 2020, 37(4): 12-18.
应健锋, 梁华国, 江悦, 蒋翠云, 李丹青, 黄正峰. 一种多X值输入下测试覆盖率损失的预测方法[J]. 微电子学与计算机, 2020, 37(4): 12-18. DOI：

YING Jian-feng, LIANG Hua-guo, JIANG Yue, et al. A prediction method of test coverage loss under multi-input with unknown[J]. 2020, 37(4): 12-18. DOI：

摘要

在集成电路的设计和测试过程中，黑盒模块，未初始化的时序单元，时钟域交叉和A/D转换器的错误行为等情况常常会导致电路中未知值X的出现.电路中X值的传播会严重影响故障的激活和敏化，降低测试覆盖率.针对电路多个输入为X值的情况，本文提出了一种的基于极端随机树算法的测试覆盖率损失的预测方法.通过对电路进行仿真分析，区域划分，提取结构特征等步骤提取出数据集，训练出高准确率高稳定性的预测模型，达到快速分析多点X值输入下电路测试覆盖率损失的目的.实验结果表明，本文模型的平均预测准确率达到了94.47%，相比于同类方法增加21.71%.单个电路的预测结果最低为89.03%，最高为99.99%，表明了本文预测模型具有很好的稳定性.

Abstract

In the process of IC design and test

black-box modules

uninitialized sequential units

clock-domain interface and erroneous behavior of analog-to-digital converters

which may lead to the emergence of unknown values(X). The transmission of X-value in the circuit will seriously affect the activation and sensitization of faults and decrease the test coverage. In this paper

a method of predicting test coverage loss based on extremely randomized trees is proposed to the case where multiple inputs of the circuit are X-values. The data set is extracted by performing simulation analysis

circuit partition

and extracting structural features. Then train high-precision prediction models to quickly analyze the loss of circuit test coverage under multi-input with X-value. The experimental results show that the average prediction accuracy is 94.47% and 21.72% higher. The minimum prediction result of a single circuit is 89.03%

and the maximum value is 99.99%

which indicates that the prediction model has good stability.

关键词

Keywords

references

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GEURTS P, ERNST D, WEHENKEL L. Extremely randomized trees[J]. Machine Learning, 2006, 63(1): 3-42. DOI: 3

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BECKER BERND. Known unknowns — Knowledge in the presence of unknowns[C]//2016 17th Latin-American Test Symposium. Iguazu, Brazil: IEEE Computer Society Press, 2016: 1-1.

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