Variable Cutoff Frequency Low-Pass Attenuator Based on Memristor with Sharp Roll-Off Characteristic

Frequency-selective attenuation is widely needed in integrated analog front-ends, yet conventional on-chip RC low-pass filters occupy unfeasibly large silicon areas for low-frequency cutoffs and inherently introduce cumulative phase lag. Motivated by the nonlinear, frequency-dependent state evolution of memristive devices, this work experimentally demonstrates a highly compact, capacitor-free memristor–resistor network that functions as a variable-cutoff, zero-phase-lag resistive attenuator. An Au/HfO2/Au memristor (15 µm × 15 µm) is connected in series with a load resistor and characterized over a wide frequency range. By leveraging the finite time constant of internal ionic drift, the attenuation bandwidth is strictly programmable via the device’s initial resistance. Cutoff frequencies of approximately 10 Hz, 1 kHz, and 10 kHz are achieved for initial resistances of 400 kΩ±30 kΩ, 300 kΩ±30 kΩ, and 200 kΩ±30 kΩ, respectively. Remarkably, the nonlinear state-switching mechanism enables a steep post-cutoff attenuation rate approaching −60 dB/dec—equivalent to a cascaded third-order RC network—using only a single nanoscale device. Rather than functioning as a strictly linear time-invariant (LTI) filter, the proposed circuit operates as a state-adaptive edge-processor. Its inherent amplitude-dependent dynamics and total absence of reactive poles make it exceptionally suited for highly specialized, area-constrained applications, including zero-phase closed-loop noise suppression, frequency-to-amplitude conversion, and amplitude-aware event-driven sensory preprocessing.

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Publication Details

Journal
Electronics
Published
2026-03-11
DOI
https://doi.org/10.3390/electronics15061164
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Variable Cutoff Frequency Low-Pass Attenuator Based on Memristor with Sharp Roll-Off Characteristic

Jie Lian, Xingyu Liao, Li Wang, Yan Wang et al.
Electronics
Advanced Memory and Neural Computing
article

Variable Cutoff Frequency Low-Pass Attenuator Based on Memristor with Sharp Roll-Off Characteristic

Jie Lian, Xingyu Liao, Li Wang, Yan Wang, Yang Liu, Shuang Liu
article en

Abstract

Frequency-selective attenuation is widely needed in integrated analog front-ends, yet conventional on-chip RC low-pass filters occupy unfeasibly large silicon areas for low-frequency cutoffs and inherently introduce cumulative phase lag. Motivated by the nonlinear, frequency-dependent state evolution of memristive devices, this work experimentally demonstrates a highly compact, capacitor-free memristor–resistor network that functions as a variable-cutoff, zero-phase-lag resistive attenuator. An Au/HfO2/Au memristor (15 µm × 15 µm) is connected in series with a load resistor and characterized over a wide frequency range. By leveraging the finite time constant of internal ionic drift, the attenuation bandwidth is strictly programmable via the device’s initial resistance. Cutoff frequencies of approximately 10 Hz, 1 kHz, and 10 kHz are achieved for initial resistances of 400 kΩ±30 kΩ, 300 kΩ±30 kΩ, and 200 kΩ±30 kΩ, respectively. Remarkably, the nonlinear state-switching mechanism enables a steep post-cutoff attenuation rate approaching −60 dB/dec—equivalent to a cascaded third-order RC network—using only a single nanoscale device. Rather than functioning as a strictly linear time-invariant (LTI) filter, the proposed circuit operates as a state-adaptive edge-processor. Its inherent amplitude-dependent dynamics and total absence of reactive poles make it exceptionally suited for highly specialized, area-constrained applications, including zero-phase closed-loop noise suppression, frequency-to-amplitude conversion, and amplitude-aware event-driven sensory preprocessing.

ElectronicsVol. 15(6)
University of Electronic Science and Technology of China (CN), National University of Defense Technology (CN), National Engineering Research Center of Electromagnetic Radiation Control Materials (CN), State Key Laboratory of Remote Sensing Science (CN)
National Natural Science Foundation of China, Education Department of Sichuan Province
Openalex Percentile: Top 27%
Advanced Memory and Neural Computing
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