半导体器件的计算智能表征方法
Computational Intelligence Characterization Method of Semiconductor Device
摘要：
半导体器件的表征用于收集尽可能多的关于器件的数据，以确定设计中的弱点或制造过程中的趋势。在本文中，我们提出了一种新的多跳变点表征的概念，以克服单一的跳变点概念在器件表征阶段的限制。此外，我们使用计算智能技术（例如，神经网络、模糊和遗传算法）来进一步操纵这些多跳变点值的集合和基于半导体测试设备的测试。我们的实验结果证明了在器件表征阶段中的出色的设计参数变化分析，以及对可以引起最坏情况变化的一组最坏情况测试的检测，而传统的方法无法检测到它们。
英文摘要：
Characterization of semiconductor devices is used to gather as much data about the device as possible to determine weaknesses in design or trends in the manufacturing process. In this paper, we propose a novel multiple trip point characterization concept to overcome the constraint of single trip point concept in device characterization phase. In addition, we use computational intelligence techniques (e.g. neural network, fuzzy and genetic algorithm) to further manipulate these sets of multiple trip point values and tests based on semiconductor test equipments, Our experimental results demonstrate an excellent design parameter variation analysis in device characterization phase, as well as detection of a set of worst case tests that can provoke the worst case variation, while traditional approach was not capable of detecting them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
