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基于忆阻器的可调有源维恩滤波器

Tunable Active Wien Filters Based on Memristors.

作者信息

Solovyeva Elena, Serdyuk Artyom, Inshakov Yury

机构信息

Department of Electrical Engineering Theory, Saint Petersburg Electrotechnical University "LETI", 197022 St. Petersburg, Russia.

出版信息

Micromachines (Basel). 2025 Jun 30;16(7):769. doi: 10.3390/mi16070769.

DOI:10.3390/mi16070769
PMID:40731678
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12297913/
Abstract

Devices with tunable characteristics and parameters are used in many technical fields. Such devices can be based on memristors, which serve as programmable potentiometers. The quality of the tuning is higher by means of memristors than with mechanical and digital potentiometers. We investigate a bandpass filter in the form of an active Wien bridge with a memristor. The filter is analyzed with the help of the nodal voltage method. The dependence of the resonance frequency on the parameters of the Wien circuit, the dependence of the quality factor, and the filter gain at resonant frequency on the parameters of the voltage divider are obtained. The dependences of the resonant frequency, quality factor, and gain at the resonant frequency on the parameters of the Wien filter were formed. The tuning of the main frequency features (the filter gain, quality factor, and resonance frequency) is shown to be independent. Under different values of memristance, the frequency features result from a simulation in LTspice. These features are less than 1 percent different from the corresponding features obtained analytically. Thus, the high precision of modeling and tuning of the frequency characteristics of the memristive Wien filter is demonstrated.

摘要

具有可调特性和参数的器件在许多技术领域中都有应用。此类器件可以基于忆阻器,忆阻器可作为可编程电位器。借助忆阻器进行调谐的质量比使用机械和数字电位器更高。我们研究了一种带有忆阻器的有源维恩桥形式的带通滤波器。借助节点电压法对该滤波器进行了分析。得到了谐振频率与维恩电路参数的关系、品质因数的关系以及谐振频率下滤波器增益与分压器参数的关系。形成了谐振频率、品质因数以及谐振频率下增益与维恩滤波器参数的关系。结果表明,主要频率特性(滤波器增益、品质因数和谐振频率)的调谐是相互独立的。在不同忆阻数值下,频率特性由LTspice中的仿真得出。这些特性与通过解析方法得到的相应特性相差不到1%。因此,证明了忆阻维恩滤波器频率特性建模和调谐的高精度。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/3e97a1b9252f/micromachines-16-00769-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/57e04bc3ccca/micromachines-16-00769-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/1d88f0ff34b8/micromachines-16-00769-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/652a79f84209/micromachines-16-00769-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/bf04fac5c1cb/micromachines-16-00769-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/137bffa7fb79/micromachines-16-00769-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/3e97a1b9252f/micromachines-16-00769-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/57e04bc3ccca/micromachines-16-00769-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/1d88f0ff34b8/micromachines-16-00769-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/652a79f84209/micromachines-16-00769-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/bf04fac5c1cb/micromachines-16-00769-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/137bffa7fb79/micromachines-16-00769-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b65/12297913/3e97a1b9252f/micromachines-16-00769-g006.jpg

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Resistive Switching Devices for Neuromorphic Computing: From Foundations to Chip Level Innovations.用于神经形态计算的电阻式开关器件:从基础到芯片级创新
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