先进汽车应用的异常值筛选

Cinti Chen, Po-Hsien Chang, Xiao-Yu Li
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引用次数: 1

摘要

随着半导体器件在汽车行业的迅速普及,尤其是自动驾驶汽车,对其可靠性和质量提出了严格的要求。如何应用各种测试策略和创新方法来检测和过滤可能带来可靠性问题的异常器件,已成为每个晶圆厂和无晶圆厂公司必须以紧迫和开放的心态面对的挑战。在本文中,作者报告了各种算法和技术来动态识别汽车应用设备之间的异常值。这些方法在制造过程监控、产品质量改进和产品认证方面提供了显著的竞争优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Outlier Screening for Advanced Automotive Applications
Rapid adoption of semiconductor devices in automotive industry, especially for self-driving cars, has demanded stringent reliability and quality requirements on them. How to apply various test strategies and innovative methods to detect and filter out outlier devices that could impose reliability issues has become a challenge that every fab and fabless company has to face with urgency and open mind. In this paper, the authors have reported various algorithms and techniques to dynamically identify outliers among devices for automotive applications. These methodologies have provided significant competitive advantages in manufacturing process monitoring, product quality improvement, and product qualifications.
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