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摘要翻译：
我们提出了一种在可编程模拟电路中使用忆阻器（带存储器的电阻）的方法。我们的想法包括一种电路设计，其中在忆阻器工作期间，作为模拟电路元件向其施加低电压，并使用高电压来编程忆阻器的状态。这样，正如最近的实验所证明的那样，忆阻器的状态在模拟模式工作期间基本上不会改变。作为我们方法的一个例子，我们构建了几个可编程模拟电路来演示基于忆阻器的阈值、增益和频率编程。
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英文标题：
《Practical approach to programmable analog circuits with memristors》
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作者：
Yuriy V. Pershin and Massimiliano Di Ventra
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最新提交年份：
2010
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分类信息：
一级分类：Physics 物理学
二级分类：Instrumentation and Detectors 仪器仪表和探测器
分类描述：Instrumentation and Detectors for research in natural science, including optical, molecular, atomic, nuclear and particle physics instrumentation and the associated electronics, services, infrastructure and control equipment.
用于自然科学研究的仪器和探测器，包括光学、分子、原子、核和粒子物理仪器和相关的电子学、服务、基础设施和控制设备。
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一级分类：Physics 物理学
二级分类：Mesoscale and Nanoscale Physics 介观和纳米物理
分类描述：Semiconducting nanostructures: quantum dots, wires, and wells. Single electronics, spintronics, 2d electron gases, quantum Hall effect, nanotubes, graphene, plasmonic nanostructures
半导体纳米结构：量子点、线和阱。单电子学，自旋电子学，二维电子气，量子霍尔效应，纳米管，石墨烯，等离子纳米结构
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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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英文摘要：
We suggest an approach to use memristors (resistors with memory) in programmable analog circuits. Our idea consists in a circuit design in which low voltages are applied to memristors during their operation as analog circuit elements and high voltages are used to program the memristor's states. This way, as it was demonstrated in recent experiments, the state of memristors does not essentially change during analog mode operation. As an example of our approach, we have built several programmable analog circuits demonstrating memristor-based programming of threshold, gain and frequency.
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PDF链接：
https://arxiv.org/pdf/0908.3162
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