Machine Learning based Performance Prediction of Microcontrollers using Speed Monitors

R. Cantoro, M. Huch, T. Kilian, R. Martone, Ulf Schlichtmann, Giovanni Squillero
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引用次数: 17

Abstract

During the manufacturing process, electronic devices are thoroughly tested for defects. However, testing for well-known fault models, such as stuck-at and transition delay, may not be sufficient for an effective performance screening. In modern devices, Design-for-Testability features embedded at design time can allow the tester to apply stimuli and measure different critical parameters. We propose to use some of these structures, namely the speed monitors, to predict the maximum operating speed, and screen out under-performing devices. We design a complete methodology, from the extraction of robust labels, through a machine-learning algorithm, down to a post-processing step, able to meet the quality standards imposed by industry. Experimental results using real production data demonstrate the feasibility of the approach.
基于机器学习的速度监视器微控制器性能预测
在制造过程中,要对电子设备进行彻底的缺陷检测。然而,对于众所周知的故障模型(如卡滞和转换延迟)的测试可能不足以进行有效的性能筛选。在现代设备中,设计时嵌入的可测试性设计功能可以允许测试仪应用刺激并测量不同的关键参数。我们建议使用其中的一些结构,即速度监视器,来预测最大运行速度,并筛选出表现不佳的设备。我们设计了一个完整的方法,从提取稳健的标签,通过机器学习算法,到后处理步骤,能够满足行业规定的质量标准。实际生产数据的实验结果证明了该方法的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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