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摘要翻译：
半导体器件的特性是用来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，我们提出了一个新的多跳点表征概念，以克服单一跳点概念在器件表征阶段的限制。此外，我们利用计算智能技术（如神经网络、模糊和遗传算法）进一步操纵这些多跳点值集和基于半导体测试设备的测试。我们的实验结果表明，在器件表征阶段进行了出色的设计参数变化分析，并检测了一组可能引发最坏情况变化的最坏情况测试，而传统方法无法检测这些最坏情况变化。
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英文标题：
《Computational Intelligence Characterization Method of Semiconductor
Device》
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作者：
Eric Liau, Doris Schmitt-Landsiedel
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最新提交年份：
2007
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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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.
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PDF链接：
https://arxiv.org/pdf/0710.4734
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